{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DENOISING AUTO ENCODER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PACKAGES LOADED\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "%matplotlib inline  \n",
    "print (\"PACKAGES LOADED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/train-images-idx3-ubyte.gz\n",
      "Extracting data/train-labels-idx1-ubyte.gz\n",
      "Extracting data/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/t10k-labels-idx1-ubyte.gz\n",
      "MNIST LOADED\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('data/', one_hot=True)\n",
    "trainimg   = mnist.train.images\n",
    "trainlabel = mnist.train.labels\n",
    "testimg    = mnist.test.images\n",
    "testlabel  = mnist.test.labels\n",
    "print (\"MNIST LOADED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEFINE NETWORK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETOWRK READY\n"
     ]
    }
   ],
   "source": [
    "# NETOWRK PARAMETERS\n",
    "n_input    = 784 \n",
    "n_hidden_1 = 256 \n",
    "n_hidden_2 = 256 \n",
    "n_output   = 784  \n",
    "\n",
    "# PLACEHOLDERS\n",
    "x = tf.placeholder(\"float\", [None, n_input])\n",
    "y = tf.placeholder(\"float\", [None, n_output])\n",
    "dropout_keep_prob = tf.placeholder(\"float\")\n",
    "\n",
    "# WEIGHTS\n",
    "weights = {\n",
    "    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_2, n_output]))\n",
    "}\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_output]))\n",
    "}\n",
    "\n",
    "# MODEL\n",
    "def dae(_X, _weights, _biases, _keep_prob):\n",
    "    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) \n",
    "    layer_1out = tf.nn.dropout(layer_1, _keep_prob) \n",
    "    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1out, _weights['h2']), _biases['b2'])) \n",
    "    layer_2out = tf.nn.dropout(layer_2, _keep_prob) \n",
    "    return tf.nn.sigmoid(tf.matmul(layer_2out, _weights['out']) + _biases['out'])\n",
    "\n",
    "# MODEL AS A FUNCTION\n",
    "recon = dae(x, weights, biases, dropout_keep_prob)\n",
    "print (\"NETOWRK READY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEFINE FUNCTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FUNCTIONS READY\n"
     ]
    }
   ],
   "source": [
    "# COST\n",
    "cost = tf.reduce_mean(tf.pow(recon-y, 2))\n",
    "# OPTIMIZER\n",
    "optm = tf.train.AdamOptimizer(0.01).minimize(cost) \n",
    "# INITIALIZER\n",
    "init = tf.initialize_all_variables()\n",
    "print (\"FUNCTIONS READY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEFINE SAVER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAVER READY\n"
     ]
    }
   ],
   "source": [
    "savedir = \"nets/\"\n",
    "saver   = tf.train.Saver(max_to_keep=1)\n",
    "print (\"SAVER READY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START OPTIMIZATION\n",
      "Epoch 00/50 average cost: 0.085103\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == 'face':\n"
     ]
    },
    {
     "data": {
      "image/png": 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0m9mzZwfOj0rZ1NDQUNVxoo4v6VKQixScglyk4PJ2Ca3WHG8iEiLllMwTgXYsJfNy4KuV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\nIiIifen/AbUAU4M+PuAVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac00eacd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa803cd590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa880e0290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/50 average cost: 0.056666\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac015d510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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KQFFijloHfPTr16+WzBVC65lnnrHKJSchgH377IFTXLPGZWVlVvnAgQOt8qKi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa88061890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fabc844e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/50 average cost: 0.054345\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac015d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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hKtyrdLcBfxO8XgaMzrVjUwKGkTAtLYAoFq50XVqVH9DTTi1fvlxto1nBfefR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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac015d910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac015d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/50 average cost: 0.053202\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac015d510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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+FStWOOXbtm1Ty3z55ZdOuZblB/RwYatWrXLKtfByoFtBggJ3FhcXO+Xf//73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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac015d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac017a590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40/50 average cost: 0.052787\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac015d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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lYBgpx7wDIVwVdfr166fur23zBdZo1uFwFFg2mtVYq1vfmgpImgcAdEv3fffd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\nH48/CBCmR1jvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fabcf93e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac015d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPTIMIZATION FINISHED\n"
     ]
    }
   ],
   "source": [
    "TRAIN_FLAG = 1\n",
    "epochs     = 50\n",
    "batch_size = 100\n",
    "disp_step  = 10\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "if TRAIN_FLAG:\n",
    "    print (\"START OPTIMIZATION\")\n",
    "    for epoch in range(epochs):\n",
    "        num_batch  = int(mnist.train.num_examples/batch_size)\n",
    "        total_cost = 0.\n",
    "        for i in range(num_batch):\n",
    "            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "            batch_xs_noisy = batch_xs + 0.3*np.random.randn(batch_size, 784)\n",
    "            feeds = {x: batch_xs_noisy, y: batch_xs, dropout_keep_prob: 1.}\n",
    "            sess.run(optm, feed_dict=feeds)\n",
    "            total_cost += sess.run(cost, feed_dict=feeds)\n",
    "        # DISPLAY\n",
    "        if epoch % disp_step == 0:\n",
    "            print (\"Epoch %02d/%02d average cost: %.6f\" \n",
    "                   % (epoch, epochs, total_cost/num_batch))\n",
    "            # PLOT\n",
    "            randidx  = np.random.randint(testimg.shape[0], size=1)\n",
    "            testvec  = testimg[randidx, :]\n",
    "            noisyvec = testvec + 0.3*np.random.randn(1, 784)\n",
    "            outvec   = sess.run(recon, feed_dict={x: testvec, dropout_keep_prob: 1.})\n",
    "            outimg   = np.reshape(outvec, (28, 28))\n",
    "            # Plot \n",
    "            plt.matshow(np.reshape(testvec, (28, 28)), cmap=plt.get_cmap('gray'))\n",
    "            plt.title(\"[\" + str(epoch) + \"] Original Image\")\n",
    "            plt.colorbar()\n",
    "            plt.show()\n",
    "            plt.matshow(np.reshape(noisyvec, (28, 28)), cmap=plt.get_cmap('gray'))\n",
    "            plt.title(\"[\" + str(epoch) + \"] Input Image\")\n",
    "            plt.colorbar()\n",
    "            plt.show()\n",
    "            plt.matshow(outimg, cmap=plt.get_cmap('gray'))\n",
    "            plt.title(\"[\" + str(epoch) + \"] Reconstructed Image\")\n",
    "            plt.colorbar()\n",
    "            plt.show()\n",
    "        # SAVE\n",
    "        saver.save(sess, savedir + 'dae.ckpt', global_step=epoch)\n",
    "print (\"OPTIMIZATION FINISHED\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Restore\n",
    "load_epoch = 40\n",
    "saver.restore(sess, \"nets/dae.ckpt-\" + str(load_epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label is 4\n",
      "Salt and Pepper Noise\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fabc8405450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa8035a5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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P9+meVylEF6OC6nqQt9ZhBee8FWsuP4W5i/pssfzEYXjroDQvk0JkmoS9tfYCxgHvx9wy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\nQgghhBBCCCGEEEIcTf4fUSHec+ZPNeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa783aa450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test one\n",
    "randidx   = np.random.randint(testimg.shape[0], size=1)\n",
    "orgvec    = testimg[randidx, :]\n",
    "testvec   = testimg[randidx, :]\n",
    "label     = np.argmax(testlabel[randidx, :], 1)\n",
    "\n",
    "print (\"label is %d\" % (label)) \n",
    "# Noise type\n",
    "ntype = 2 # 1: Gaussian Noise, 2: Salt and Pepper Noise\n",
    "if ntype is 1:\n",
    "    print (\"Gaussian Noise\")\n",
    "    noisyvec = testvec + 0.3*np.random.randn(1, 784)\n",
    "else:    \n",
    "    print (\"Salt and Pepper Noise\")\n",
    "    noisyvec = testvec\n",
    "    rate     = 0.15\n",
    "    noiseidx = np.random.randint(testimg.shape[1]\n",
    "                                 , size=int(testimg.shape[1]*rate))\n",
    "    noisyvec[0, noiseidx] = 1-noisyvec[0, noiseidx]\n",
    "\n",
    "outvec   = sess.run(recon, feed_dict={x: noisyvec, dropout_keep_prob: 1})\n",
    "outimg   = np.reshape(outvec, (28, 28))\n",
    "\n",
    "# Plot \n",
    "plt.matshow(np.reshape(orgvec, (28, 28)), cmap=plt.get_cmap('gray'))\n",
    "plt.title(\"Original Image\")\n",
    "plt.colorbar()\n",
    "\n",
    "plt.matshow(np.reshape(noisyvec, (28, 28)), cmap=plt.get_cmap('gray'))\n",
    "plt.title(\"Input Image\")\n",
    "plt.colorbar()\n",
    "\n",
    "plt.matshow(outimg, cmap=plt.get_cmap('gray'))\n",
    "plt.title(\"Reconstructed Image\")\n",
    "plt.colorbar()\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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//pM/+6MvPMkIrFyHdo6/9e2H9mOIHe200vy1YD8UX8kifcilmD62mmH+7jcP\nLLAOI+BDz0F3fo87ueOOwyjc4Zp0Vwz+4DfVxYfiLU2FXkNnjy998Oqlx1Z+dMfk3/7xu4dfgt/+\nRbN/sMb2AVLoGh0KFjWccEMq8y5Fu5l//GUfgSWhErKoQikFNKyeqHtoYpPzbNH9ahbGIB9G7UHC\non2yGSMcOTNASCcMALXyfgbdGdWlCM83YJSuUkQrzpuDiONSGAxTHpayhx3P0lIc1Jk0OTmhpBkt\nonHKYZAgExCmyztpNVvEegZDNjCLIfqajMs00D564IEYe/xibNs4/m93f6X07/nvj6788Y8a7enL\nNGA3LZa1Aha6hI+luzE2udNMnig5Yu9IKQR5SGBX5hrtJSS/fpBd7p1a2rQW4c02IpAUAJc080vX\nn6+jHy3Gn9f//NLwu9CDvzL9F7u1P65+BKjOcCvrNVy9K6IzQIyhcdUTlpY5h4nZQPV3QYALxTC8\nzff0spJxW7gp65IlqlUABRpAuGCQVEkMV0RI99mqE/oc6SYAGKrCVJmPco7k1KudbbGFUboh6SYW\na0iwAiW6jIb1G6wq1Fkf5xjPNlF2D/MiHsihfoyksf0C0a/FsLiDwh0FoVM6KepVgO0KGvSPs/f/\n/aHrtPD649v8/K/eHHm00LpqZ+IgNUnqKts9jT+YC8rm0tjGbafLFweMidYdGkSuMcYHZx5zHGOq\ncu7kXSO1kXNGOtvMXE8zBLjxhx/458+RU9Abwu3xL049lomdtoTL/30pQjx5+NuAYnGnoeSFvBCn\nwey87ki4pM0W1LFIrgOYaELyW06foN6KCuoYbdamep5TpGHJKAGAizroyTGY4u2uD0sAtrI+a8gi\nPeiA1VEIo+O6ybzRphX5nk0o3rcHw0kOX8NxWIP9lqvCYx6+17VTdB46pJKbQTnJMWYfNCx1o2WM\ntdRMGNaNepolowKo1Dm4msmBnePWT4Z/vfXCI7/6YXn856OP0L/f2di9Uz1zYjR/GgjELtTpT9fN\nXt7CSu5O9lKHz2+UaZoyNZBgJpffhP6ln2dWk+/NX1zbXZKhyUv4gbPd6y6I9K3f8IlR49ff+KH2\nvY3I2e81sS837njy6v6U/CmAJvwAkVBy8M66yuzim5yd4/1r/bGGsuEDrkdOxnAuxCHGTI+UVQ/b\nEUlIlz2R3wQ4P4hgII5Ift9kmTKC2qEZSoGzDFECUCGXEqWWUy8ScZ51CXDVk1nbixo5AYITkgnr\nHiV3mrGaG5ZpZzniiYCMhYYZAMWKFG12NRo2TDQez7XgCGqjyawGoRIA+40i9MIP9vzYK7Gn+vef\nGT55Mg/y/WR7curyPJDlQy09Um7L9TIxFSuvvDMzxntFbHekqUNgO24OCeLEvmdGDGfV2ZD236fD\n1yaOILeamRyIeSaYJ48f/SPhxB2dK+vd0+l9j8CxsqJdn30dGGPowE2adn9URxuVWb5B70DmwYQU\ndJJRcKsaWca1AG6iRV+hZyencF52mTDJbMoiQPVk6ACXyrJoHFELmAHDLmPksBhumGB424jUmwcE\nK85tZ2a2b/viH2HG5vbgVrDreHCgESjwW8oUIhStdbrq5iwUqvPtEUfigOihXVPLeyIoQMAboFLT\niwW9fg7zkzoYfY0oHPgY/JGvvgh9Xr/55Xnrjf1jxo+VoY1PBTg4wev+wX4MR4Xc6f33iM9JgxoP\niOqBeo5yAEXtFQ1JjMzOw1zlZmUksxyYs5n8pfHQkEB44QQ5KKwHd9wxc/X+3ziVP3jj5TwXCx8o\nfdE7AcyfVsbSiGnNpMZ4jem2mN1La0W6RoKCpoFCqsLqqKDkPCVyxB9UiSSBOzqQu3nLBcCzHYOi\nlNVmoEhEyKNuj3CoPdq3HRJwYqI3xNT6CKVOqtv5fu+JR8+NjIcFWQU27DD2sI1mEqoi9vERvGL7\nB6NJpwqv9WZoQOdM37Pam32ko1sI3wFYAEG+p0eRjgB+FPs6utyldt//m+ozQ8Zph1zcjOx+Ed8R\n33/gJujvrqc1ncxymL3AuIusDDuaJOYaoUFEwH9yytIE172FHQ4p8YI5tfjYZ39X0hdzR7NLFcBm\nF0+S1zH2zNPQRDo/cShbevfD24Gvaz2mDQw4ZbSQKMUoe5CVgy1E7sb3gkgjZtRjQA67eiXbT0DR\nEPOG0XRPtSwRKDDnAxFQDu6woGuVUAEjgxAVXc5BEbiiE4YKAlKiaCMa9/txhFjeXL25/fB0IjLl\nQhAbwCFlanGy283lpYnRCLOPaAjRLowohtDygd8ejOVT/QjcDUMaokoMBrUJ1o1IImiA4/EzD/15\nb089YFK3H0xdOzgEk58+9+PtKPnWSgusoZRYZsV6nDZj+/oH7z4wsabFc05aZfa64L+tDeXiIXoX\ntKm8+vThT92ixhz9vXS6XjWLAOxa2Jc/dO4WvvzZxZ3vvP3Cv7zQ/siNfciPDjjzpdvAYb7Z7XXS\n5lLDcgW/MAOhWDW259HcJtMAnBfHC0x+xtIwtDuvNxSHCDxTjMARbwB0VzJQHaE0uKMqbVOqAXMH\n9kmH6+ZxkEkF9Y2W2R4hkqri7r4mvTMvV7Zf602nvARq+jGDyqKNLXyoH4FXmvrUJhbrlEJb1Qxg\nglhoQcOBP9EUuwf1FgmJuAGgJkLbPHhBPNaoV8cJ6tkZ44fiL6Dts9SXX/toLPvUvlc/+zoUx9EM\nrep5ygx2wxYy2hcztYVTS+rxDQWU8zdL7VrmnTXqna07Zg5kBq9En3pHu4uvTw1pYLijHXzFzmw+\n6NyNMC1t8vDK8w/Ii/fdzFz3r4CtmG5m+xpjNPavlKwOZtYnDqRh09+Kh3FAmTqLeR2MIB29mQCU\nLXTiEkKDwgIxBPoUkzFNQlR1EFi034vXM2yH8oYtGMNAGbL7aNpC9IlbcgvQx2LwtfI70z52Zag0\nhwpaNRy254fxODzU/nZfKF8hH9rdieTqRMQGoSNnoF7UYuQ4MmRalE05ntj3EYYIZPDHf/Vjfuh0\nOy3qyCOIabWbZy689lDtOHR/9MnrACGsn2Y6I/otYCer78TmfmWDROLVchybcwXQ2P/Arb1DIm9e\n3RjNl+Bk5Z2r0X6nupUb7nRAMzd443Pqkqt/7Zx7b/7is93gk4n0AFMDu+ADK990OiMy545AOalv\nbLF09p4OuTHd7p9YAh4+0glWMkrUQcpZh/TjwJIwJCf7OWsP2BF8h896XZzMJ2OLm249tEMBJh0v\nJpsgWnbPGZY8mNaQS1O0uc/ubN7MIWw4Yy4NwXU6MTSAor2+Hiv/EJ19omBE1MhYfGOU4lAgUcIm\nNdojANbXXcW3MgSbDqMTwLPdOLjyyY8x6RFunP34xBU43SpTq+LREtv4qq+/DABxozbJTVt335OK\nQgpf4dB20OtRqWiSpZtgZFGLn0kvO9v0k3ek9eW9CytvrY+y03dGfmrGwT0fSP0/8/tR4dunLRx6\nZvUXtCLxzcsam7ntl141ALfFUdM7fruUAhRB5yZHzj6M6GvZJV1c1gFBlRfRvN+tQ0TRT6FZ5xpi\nQcAMO2xaBLxr0kRoMuHs7z1/0LFtYghjeMFxUcNiwCIkBIHsj9W0Tj5ZEkG42lApe+ggRMVq8HQ1\n6jMwMdyXdtdU7jNwjRbzm3w/BwUWBLqUmDfLJKwYGUXRfSoyqRAcJeF2k7RB7ZlujG4wz3zvy2+H\nWmr49AH49N3rY8zQTc5pAuxEdDi5lrl+Q5pM9tV9Z9NxVHYCvkmmMBGY2Bvb0MpRBVpQRGL4cY03\nzo0eOrwpHvh5pwGk/+H/TeOVzxSP/8ruSL99qP5HB5X4SPr59cZ/fFIACdc7+Vapxq0Zltrjo3/0\nqTupq5azG0EtiwOttn8aRwMKbtTaKuxVVX/HEY5v2ES3IgHK1qBQ82pO/o5nf7oC2LSFoJhJwQ6K\nG8BG4tUBm5Vjcpth7Ywtv1IpSaRe490rHLyV2unIZmATWOQ9rPD035d/58gSgjZtVg0ASEYCNUgn\nFAseKB7TFewrre2BBlAWhQKgTelzzs3sDLj7Y5AML0x9Z+XyxlsV+fbVd1o1sLvHr3hjEKCG5uds\nzhxw3nh/RCoZSJmNgjJyR+9CprojP3n6ULTz13MAnzmPbsXNrWfCDHhn2DpQPFPNitcmvrjNt46s\nzbsHvmvKp7fGLihADoeMpJfqJGq0FX/iE+emWldaSiFuF9COAdJ+vuYzlEVqnh1ja23Z8eJMLbVr\nUhgPiIKIRjwHs/X/+I6GQIRpobhnOh6rozTI451IUF3Lh1xx1rBChEu7DeCyVn9wj41qDN0kcmhv\njLve1v4Wpj95srfRGEpBLkfXQL5pR3SMpvIKRPXck83n3rPzD5CZnk8mmqBTLY4WD26p0uSNi5X9\n6Ut7R6DlOy+dvyChb50GmZYE+dBCERpwkeErg0kOUujVwy9lth0LAGhM2n/pncLgUbRUrfpcPGDO\nKh5B1Mf6bROc+8v/8apUn2Wa+5Mf1T7vf7eW7X079/KDa/5PPtUDdnE+EpmPI0ZcRG8/OPHGj/T5\nZMro9XJWOgnghFOANF3ReYUvDnxWQvN5fq8pMqoSBaYTui0osk2V11WZh1N9psfBEZm14QACVmAy\n4cTPTjItWrbLD0OtU35/J2EGl8ZwEY1qWZs1tZ3jkPZBsh5NnaCvdq2wMw6oQQpYADGp4PpUjRqE\nxEzzvb/Z+0Rv54gQ1DjNASMG090chIUDZ76/n4YXTs0ev3hGvDzXe/Tyb+e/U001VMJLbqQp4Kip\nXBsJODXXRCyGFrZBbM2rTfk3Pqbtx3Hueh4NzuESLtclfLBHgwb1F58Zs7vpISilj7xU4Yayb318\nqxe5cfKJnRHgNyhHLbWGNDGeA1f/vrVoxDeLlYlRlZUjQMIHpQ12gHIKWnC4w7WB2DsKgpjis74P\nMB2NNob2BHzXcRmGNhnAaDgBo1HZDYGouaObK+8X0q0av51ePSE3GLiIF2/MdtcTKDR+udQXfHLr\nJHQkTVnjz9/aNSbNaDBAMhLYm5F8RBiHCzJJ8mL97cW/sl4/EMF34dDEwTUk51l/98fdW1zYy3hf\nWMl0yukKOA5qx1rPgrMS5XgJrcjoeXrR0Ia7HYUvLWdqUWh5DMSafdErkmZBhAfkBNAm66xazoSc\nlDn2AkjOhq9tlD9++fwPzn9i7ZXf+/rRd13h1PMvlZjGJg4IjpZFax+ch1Hz0k5X0JK9XDCuX55x\nJ64Dw8WWR8oxxw7i5UmkuQjk8+a6ScdVtRAAiwZuREN8irD9wGnmbJzGQl0AWz5DASbSaqUJcM+m\nrKoZCr7GTRC9+qF1DNNlBpbWM964LxPujY1rtzZaL7xz1U6yGISQ2koAInsoOvACPEhgfn9AbFDj\nQbZA7WlI11PAk49oRfBFwfpoMDdejDU2j1K/c4fwpXd+5Sd1cwZUgM+lbuD7+lYNT6TclT6pyW/4\n9HheDBGgJM+H/CbKrVzcXbxVX3/6H/7ZbKds7WCvpQJAUTPpRwrrh2xd+qVwfD6VeeIz5FLh+HGW\nmHwQcFvLqpmVB3J7sXKdI91D0fN6R6WmyfbGFEhgRF7LMhKFxkb3Wzvz7nGiU6fkilccNIATQnbH\n03E5MGAsyOos5OAocDwEZQ2wNydUpfoYt3bVIPJIkkCJDZXcKpj0sJNFZ01qGYUioavvwhWnA0eh\nFizmXRYBMAycrD7kUU00tooKANMK1Z9aRyO+JxCCAgPwXhYe7409TXn2CyfIZfzdW7mfJS4sfeS8\nusKD7I1MII3Xh9V7NozEu6sHiwKkpgzNrgViEyDbGrSEF8po/ua21lL3zPTf3++HrfmRnn/qezvp\n1Tv/1R+QSe/mydVf2P3+3NG5x5/PZL5DI603AIyTObTsZXSKWG5X33/djFVLQXy7Q400toHhSXSx\nUsj3xykz/fwqcXKft5useimi7+kgojIY4vLAhSKep7m0b1I2wkiIYDgUmHxDuO/NocF8g8d3Z1yP\no18dUwTv6jCMW5fhHW8ThkV5MCwbO8DBh0yUK9V3vNAYeDAYM6iK0E6i1QyveQj18MFyMZ2wq5bs\nJ1Ogui91FNn58UTud6fi0Og4detMjSjHfi2c+14OgJsiXofgrP+QEbX2+3HhbZg+Gs33DdgtZgDn\njMIzh6V5WuV4HIvP5ApHVVsY3ZDDsgNIFlJjOe7uhc91/yfzRjkT2yHfI7FFxmPfvwKgY7k4MjYV\n3WVbo8Oz/aECEWWnevRwrpbHATQQrRXRQ0dEYYSQmgcODW1yfooYmAQQAMSZEuFDMSaKBh4fIg5O\nwR4UZ1HER8A1Ub4Ytc9k0dQgjzPp9ibicP6hIb9hn4rBZEWLGh4tbOT271MRKHa0dChaTA5qFkgy\nYD3wGB0gOBc6kGmMTr3/0L5DI1WIFvb7MCjZzSPf8ZjZK78azbb/s/zgmYijQrWN9F0PMhmQIdET\nGm5V1naQ6Npu3D7MVy6vSlqieNBNgSXiKtltFkvm5ecxPDpSzvlVl0TQ9yl9GgdXtVdv4snx7zzw\njeHvl80/2c9dzWRY/b53H15f/ThwN5sVDK646c10R+GIpIAAPQyGZZ1GKkBNYJQoQBHTdE/7Vubu\ng8luK0ALJGZgORACGiMpv29LhIexjO4RsmxaWuhArgUGkWq3PbbKJMxshky7RGF0O1pqAJCDWAMm\nHS4UdtA+IxQPjI/e49EiseNj0ZQT7SuAGXRaoQpwx/GyAtngM4+MppcEB+9v4QZYZXHtL8ZuJB59\nZOv0Qu5XlfaZ4YnFTXcou4K9AaaUSxojxSFdeEt/6H1X8c1m8ghRn+noSmMTTOejBrK5PiPHsyuD\nGQR7+RASwkLn3eKhAAcY9HuPh7nr9Bv19i1X+5vX46NgIdP84YeMJzZ+AGSIUiuuRxTIl1hedh0E\nDukyvDAr3hXNAgdp9RzMdVkEjr+jRaOTL6VH87qKIuxgAZghb9su0U6aLd9zDYBBXsxP+ZArQQiY\nDYlh4R1NP1bCk5wPxilnxjVUcXAzP8iiytRSaziW4g05gqesPuG14SCrLZ1yfJcCoskkdvkejWmM\nFvWAFvrsggfiyqQedsG51Yv7rp74eX/huS9df3Lx839D/cIXVh+cqcbb/ms6uAEOXotiLtGY3nfQ\nMIfXKY2Wo+EmM4FoLSBdP4+HEzE72mMzQ5cuUXw6ey0RHm+EaOkG4LQr8d3fOjTX/RxUhcyVmeXC\n5r1z+zrdV1PLtwOMQfTIWsStaAe2En7UYvq+YxNYGfouMwEwLCzClSiBYwd/1nPuUpMk7IUxYScM\nYg6IuRIf6ijfLLaCPoBdx8YGpKlRLi90QTCyWk2YncU53G658S4Hpc0BWuc5rcy5cM8QJtWwv0cq\nV1/ffWd5XQs8bMc6sox5hAs4PurjZBT0wqolI6GDi82owPtMA8FyYOWN2/J3ttlN55MQMg7/j8LI\nH88f/t5735xvk9sPgokIQ+MxKxOvEUpZd3o/i2z5MXRSvaIlh0B5v29XrYqUHDl3R2H2uPg+vLyP\n191XjMX64yD1CyfBfX/yz+9JP9gomjHinsy8+BPLi1WQN5M9kHIrnQ0x1WNQBblY61HbLEtnG5Qb\nhYxrILEdgdo6vOMPw7XNynrhlZbvWk7XyNi2BxqyDZB0AsaqIRm3MNbGLIpQWZ9xOixodIQJeFqu\nWN2m4d9wmtubKG0MV1MMNgbDognJ2XiXNcqo13RFqmFjlgv3h9Y8LwLKRDuI61ZNQGMoVO25mGPW\nOygUo13EBYunW2jLa2+A8xo2NPOFSwP5TPvPW6Puu28f3QWbu7sItekbDjFQ0ANqpegdu/O62SZj\nb72rgKFYjToeERaeV1fJcPjAwV+CoxmF2XtSQJElEPt76NNj5udmMsmpJfal1xYS++IZhm8OE3ce\nmwTlFh3xpmEvlhkOo0vvLDDdNb86NPD2wtHTwI1Bbm4C9kjlB6+s+WuSZCh7ktUV1/OmA0KSsCG5\nhg9c0gtYDERBNMJ6VJeASQgMeX15c71dzyXv9d9Wq/2h030+449VClltFzVEjbERPGTSRtwookIg\n9ESBkcqZEHJBvEdQxtBqzKejSj1nkLDMw04ttZ4OugLY//SD79Z89oy+sO5cnBmarf+Myb5+D7M5\n+PyLk2DYnTCluxveHhu8uoic2r+AVZUpJl5JFtpVkOrSo60GVx6uD0tdvJP5Fl8qO35r7mMKUwG9\nkXNPtfs/9i7fhZz+4bmLW/G1jakIXNAcrH8QWGN1hH3tUL9WRGKHVv0IGifUYaxrpva9eQJAFC3v\ntCPRgbK3zFLcezuBQVtFdS+zm7ZAAu0SHkJRaJelbJ0PBymonabRDCHYENC0YfTwrRreKf2Un1Lu\nq5SDcPmkN0cE6S0KzgelRHerGJPD6exookhlaRbvVgzFNfvbACS0/voGFaNty+VsQdEUOx4QGKSE\nTg8IwxfbtrCtLc0n75vMDu+07z4Y6137SSX6tU/MADjVvQZ/j10tqjXSQLkuZjF7kdqbuzf1egbM\ntdKLC9P9XD7Vby0suf9Abb7WPzfdmX7G0D0AX+s89QB2lS+NzP/DodUnv91vdm4BE31XXM4ug+Qy\n3qX4FHWY6O0nHzydp81MqA5mY+rSyCIw4gMmxsi6ZBrDmXEGoA1EjKpuEu/jAFj9SBA4juOzPuJC\npmOgIasovrqnagHQSMm4jN5ntS7MjNrjc/3m1tvo28RYRDTwGNyRvQ2I7TXIFAIJpAFJGpktxXVC\nVicTAHYz4WwcaEzE6OCsHyYjYDf0LFE0cR8s8Q/z+sU7Vli2c8N9+x+CoZ8xUCNW1bzxqVWwunxl\nxDlxne+ww1HBKJVDup9vxYVw3xURAR6+LJUGw0WhY6nZIfTjfJ73N7/7Ue22VL0O1GHhD96sH4Gy\nK9Wjt4ibH9r5+O0Hu1vPJt+9LK8BOa2XosyaYchiPbp+daMPWkdctMXqw9gogDdr2sDFilsL2qiY\nnrV7WbILohbvxQcIoKxeyHiUx9Bhmw86IC/1w4DFEg7KhgBW7M0DgZIf7S9HDqANg0cmQarp2BuD\nmAGDsdpwNAGVQKdV28FCP15DKuuNOCuMlR0A+YFXRcOC3MBwK4BgA1gCwfuyRlsCUM+/jMxeqw3G\n7d3rtfhs7sbHKs3ZX7/z4Pn6t7KAH4HBagdO7iekvg5tUwkfh9pmINQmOxFgnmlSxdapsgzvesgc\n34RnIoz2mQtdsn3+DECoW1b8YzFML9/5MrNGvnzyP6V150RBID4ynwQEUsTXeYPDXBl/I5gYlXs0\ngA29PbpobgI9OW6kAmt7Or2vFvADxA60HNeHzFXYqQM9CkcMGguDMIobUMlGUTuEwi4eabE2iI1F\nxN1ox2jdc7JNbZyfJPZHgSOtYdm2jP/ffgP/7wctaRGph/sujfsxszK6VvIswbN0vHlyI37ix4ZB\n6iikpc1uydURCMdMyIMGkAgN4M9/vpLpF/Z/8xdXwHza3RsaIFZh7he/8/4fzWSq6D/+dC+nFZcj\n8RqPEiML/sh1luWb8YYvUPX4oz8vyQf+83BX3PvAxI+2H+9QYOilu5eqt4HW3OngHzqK62BqFFJF\nHQ0ADPsAsLZr4YiJc/nfAK08UEvihRSK+gMiLEQ290oytlUY0p2/fH0wOvA9ptdLKjw3YOM77mSb\nqfKtEJpe/8QTmH38RnFRRBPwzSnXWz/W7YrDa4+8kLoyg33197fg7/7ScuQ8+bfYR97Z/03mV39w\nHxl87XfmSssDD1bgrkxGSchDENIrBEWOijloIuGlQEiDZtzUsLgzavhRQ7IRv09bkskIsGxOCGB/\nxjvcX5x4r7J737VzJ/GP3zNp/NabQ/M0MEeTwB0GuaW80xsbWNjFcKCnMW9nIs0wTb1oAy8vhfee\n2jc2BN146RTYqm6/8fDN+Yzg1slDPVA2VAeQHQjUNNxyAyhU+yhh865Nug7YdROezcpLLqzsGVqA\nVMvOFGTpuR3dskEfLMmw0YmUnEIfgdbURKLRtBOKOCxuHAdYWajOKHQnDnWo1b3Lk1UjNhqQT2fV\ncwMffDnz43Pte0de1z77t3Xx6iNHTYj8Cj12Y+rSBvQkHA4gm9o1eFIw26hGgEWoXtE7fSjRLuwB\nYZUSwgbSJj2yg2CQH99CccIIDZSs+uBFpM2MV2ei3r3dL25VuS/dQKfnP3bbUmLEzT0JbEwb8DrM\n3sjknbSHXd+lcx66pGGcIvUAzh4L8Kd6LzxUZQ7XKhuz5+Jt5Y5g27zvsJIEdEgTKBPTIm6Mi2BK\nVxLjmkUTPu8xFmCAbU7Xe8GEFecbCcnrG32tiqZQfNMFgPdqw2W+tNNMd/ImMbEW+gFci6a4bYZ5\nC9TP7uyu1WP7dre9c6dG7ksZDXjNSwadRHAaBvcTTx4q6/cpzrPfKp79MLu3M14er+974LsHJtU/\nQFWOUy04D2QFD9C00ufDWILp5h071hBAGIGDVN3Tg8A03SGZRGjgKDwcI23eAZ+6NPHGvge2nvvC\nV+gP6fk7s/WJm/aPvGOdRSn1CphZpRTHldHC1gFoVyyouS5CvHLIqBZbQQ5E356IP3W7g7+ZTt1O\nNj5661b2VkSa46cWZr+SBYTnhS5tsC5l4B4VUpaFyAxiYg4NfJBkEGIBE5i+0aNS6OgOsEbMQqfN\nnezSIdCSWF3gl4Tme0FeTIUzK3JWd+rufGmPGwFDFwp0ByHfmSLMJke0pJ+/2XG5ykFvkTOaIPZ6\nz0jb3//gP/3ay2CB/NGxnpIrLnW5aKx+9CRKQVVWTTT0KEe2tJSg442E0EuoDldGaGAzGq5FfFe0\nlbxnCIIhtiwj6miHBhYDni0fOhp/buKBK8Pijb2dHqxfmbk47R956mRtPg2MpG7lbxzsCkJfwqCF\nthsEZxZ3uXgwsisAI3sA+vT2xzbDH6wdXR0LTyTTnbt+NIwgxQP76q+5VlzVGbSWZMmAgOIQZcsS\nRVo2pgMYDAwimlvpShqJq0hSxyfXFZzgDHzZzKNAgSJWskYjyKEt1SM6jWJkKd3ZPpamLdQA5kRr\njLb7omOmS+X+PQs/7J+I3RqbO2mPmlFwMaF8cbe8C37xaO3Czj2/nI8/z/K1X/29s8Ur13qwawUE\nRcMlQWmCaAICfSRpuV0v3kdTMoioEVHybHtzKbR9HhUxGmq1+jIpKQMZJM+LoCZy16utNbOwo4pH\nJtZu341VHg7Gs11gdssSmt5iTc1wm9uhqWryW63hJFzYEThwWxftJT5yUVLZUz+hf7L3hjIK/m0r\ne1SZedOsAIiATEYPkx6C2tff/uYPewg6TkYAZPAkDjiONp2pSAB3JXuIbRWMqUmBpEJjUGpCILJc\n83Yj0I3eJT8/dJgMBWRsNwjaGYWMwoCt3be2DFCyK0JV7BOtyGLV9AubGfeMCZdAznr897OrEEF9\nb3qYWVnam3NPvfPRH31h88UkdxxV4pCjJafKCTfmOkzIUM4ti8puRyDLskFDsLrBwEibstba27Vg\nagucvMtNB7rMamCoDbEbiQdje8leKvHbaPJWcgYfX7o6JZbz579uS7k0szhUJvMtnoehwI8llVwv\nblajYAAuTiy9euS9mZuZlP/5K5+4kc5eSd/4YHCxePHUjgs4aIB6hEuiHty69e+7v/0Am2rpRkyT\nLCUPTB+j7UDzW87t8VxrtJvcHInXgrg1i/Ae4O5o5cSmlMNSgPS2GKrmYUzUMazp9ZkdoCDXDu/o\nOXZ3e4Lh137h640o/zbr8rdKqIWCGdv4pflp6NAb8f98TB1MPz6w3nAq+e5t7LwVQ6Go7QdgrWTs\n36lX5REiFBG7HuR1uKikQUpKegOOdLgJl8idjAEFy3VFth3jQxMC37zznjd3y4en3+7MBO+yzNZR\n5YY18+bE8d2rRQnARn6BmdpM71JjoprrD1G93YydbiYbLpsAYkse333stTpEXzuRlDCMnh95vzwy\nqhfm49tA83mDdsMBjrXm/lX8yEOCRfmBHIXZQbwCMMKnen2rTpw6PbT27P6g23KLYhOkOhDUBQOR\nXY2HymljFuauANJ04/3xKuNGG+k1EmToZBXGpPDYwuPYO+iPeqOZ1BNbMlqSrdldYHlP/+G1yNJP\nTjz/y1fOXkqt/dunA2z7tndT2lE5gdKK0IupeAfbeOdnO/fdPppUOC1v63S76eVBHcCMziKIx7ql\nOJXukIzXqab5gNIwEpxeL07p51/Z/dPfnVg5uCqfsEcg758PzjT6atADG54umOXOJEyTPlWz9zLW\nRA+p7475ImaBLby4djxHDc/0yKdHjGmHL4SVQplw8Wy+CjzYsiGZoxAqHPvvw9S5lhQCSILoKGUL\nwOHYXYsyYvr+BLRmvsemO71YxeWdaGirgLZQVE22sViitb4mbg8zqzOD5JUDrRzAICCw4lLJZp2d\nTzVYFj66ws8Wv8sjaJ2dXR8C9H+efx22H21P8Xe9km8MuBMf+dNTz1dnvwZO/lhEAx+hXdkbwl96\nGiweoa29VDDWoD3A2jkcMJLdwX0HQcJYjhKhOBVZywYGxA+iiAEkbO+N37s6tP1ynM929JSiyUrh\ngxVp8Pb5vRNgqm9BrB90nLS5C98ISQvDnG0+n2olezLQbMUI5ohHnksX8+8Fp/YfV8rkwp0/pcWf\nbZ0HOLw7hCZBl0MjwwcBWffhHupFjYiqQR6QEzbtIkKXE45981srE/XlUuTWOEZU3AiSAw6xI6zH\nCt2Zbv+rP9k7eeoctpxtzgaH6oiRAMtYYTQa4W/MbubWr37g7d1oZrl041T9o09BpbfBqz/3g9Hf\n/gtK/bYRTN68ZyVx+t+F9c1P/sc9Iv3gQyjdTkBUfSb66jOnxsABPrbYzVhDvTCABMoARKrTjzuD\nWJfFWtDbMSsSbcVartnmJc8BcYJ/4saFh1eZ+Bq0NDYCEmt1q2SUw2NJcB00/CEX9NM6PVgydk4g\nfQAvQEFvvJds8n2wffzoFbVz6PX3lYtvF5rR8o30UnHCxkbf3cb2PaOgCSmGaDxB2nzgKkIfYl0v\nIHok1UoBZOChyWq8cSzanE8mYtcfW+JQA+YxoV5yAOtM+tz6WB+JvvzV2JMHH90CyC5p8/PAcxQQ\nhgts3SWGId4lhjV5e3L8mUyrhb1uXGRn33jQKoQXoYu34ZXJH3LjD771mqfaH/5uxmG/8qEY3B9W\nGW8CDxvRUxsGNWLacFNu9oIB7jVDADnCIQRnBnm/vrzkL2+88bx+ZTBoR70GIgOi+lrPnEqOix8e\n3h4X66996Sq5W+8O3xMqR/4JRDum4bVN7JA34U5h6DCmOFC+2NHjWDsHziQ6JZO4mJ1Dq6OJD1Wx\nhchw7qk2P5L+r7+qARK1GUdREQwJ7IFDAiSUAMEJIdATLGClBDLOziWKjffSh2ZaT4B9gRpS2jBO\nrJugQ8gDMmNQQh+KPVosQhBMZtD+ptCY0LJAb5YILzGiZ3tNd2ykH5uCIvPjOAWjiXwGLFzpZ6sT\nH3oqcWfy4w8tXbjv/CPQL90R1KGvHJr7Gpz2XAlDU9U29J2/SuwDPdT0wzAOx3geg4AV4bZ8EiRl\nBDXQ3U0JT6us1eW3lAw9BBYPGZcPppuFyI3rHNQ/fObh2xMXms/n3ibAu38G7If8OJLAJrix6OH0\nybFjPBfnqyEi2joqg3TjdfvoXSWXFbob0ountj7ylxudXxNiPzskbkGAsmOQh8cQ37UVhkYRhvUg\n1zVwLQAKCAp+dMuLJlrBwOLoYZv0aNGqlbkeTXSBH3iRdljlzbr5qVNnMx0tMFGPQ7qF3QQCgoAt\nm+gOJrOUIC10wfBeIg5HQDJR914Bwr6PT+yzuxS8UF+QHxctPfIA+Yf8kn/wocwOrBupvg3bKtbf\nPn5Pse9oCEswGuc0LKoNXLPN2w2TpnQT9vK35444tu4nMZgiFBMIqS88/u7Z+GUBeOck7w2Lhuf+\nB/2FlTu21dsuALrZ8gYixEoTpdn3jY8gQmI0FdXVZJWgVNATH7g497N0ILwSLyXPNmKrvzLq/LR2\nOFx+ulgBMu+FOGLJnukKriIpqslbjN5HCASiAKN3qxDJma3NJYgYcAzV83fhBLbG2ywNaCfRzTMR\ncw3NHcynecXuaQ6O58RRWvTBSLI1OWSAwjIuaYbnjsDed8o507jaKPHD4MA7Pab1ulNoivPT69tF\nitm9dOtL2N1k86c7d8ApsTdkwmYmefSXf/3BOGfBQIelmOElJDwPaAkKo8BY3nE9pginsyoGpVFa\n5VYkVwWnL7xIJm8Rd7+zCX+3fMDILj5y9wVZY14+3lqLA7ky1TBC2uxtO0b5Z1efKwspghpJlxOk\nZIC3xXc/NzG9upA83hfE8qfQm12WE4//27hVe0YDrA/hMBbmPI00uAgc8oEB24To8r4cghQPEj5E\nKrVVCJ/XfUVquUI6SCU7bMwG0dBit1BHaigzpRBlKzRLKPGY4evxagooRyE/KLrrs1wLGq/1O546\n+TE9vg8kGusw+OfRH7/7irjz2HLz3EuedrPG1kc2/vvy5oWHZ5BLaItOasluNH23Be03CH1IkpAB\n3rM5mwglQPqMK7lwyFsxxAsaadhWQ+AYxBSCdMBLR141ElEWSgXm0cqyVyn93InTKy/eOWpDI28A\nP9LziR1xt/vKvtcctTv7xVCqAKtQqSMjJji0Fb8S54/eujAttkeHPz7JrRx28B/xd//owa4CGFcP\naAfrobCG91gahRCf8chQlWnbAls4aloI49iDbhUVQVQdTZq7cAijJEmBOoJzWiNI92FH3jn01r7F\nZKTVzHQIbONYDpSholSvHaDMFXx1XNlXOtnIXF2Ol2d3h3kVsNWPJIfvW/qPY/v/8ZSl/crv/6Ln\ntfiHlwTv+h3/B+5DumHGEkFLjtTcgbrXDkXK9OGtUOxhwBXkEDcoSEEblTYZbFe0fDMN85LfBcPg\nv/KlO53Wa7eM9566RvBbOHJ3bG3yMHj55ok3YECi7dR+Vi8vDlbZxz/Fa2pUyE1w8oDZt6sBvUWt\nP+tfIA5duHD4LnKy8IlTV+b33n/sb/ZnqKNA6rq4p0R8jPVtCoIRFxLsoFshWFhwAAPCThAHkZ0Y\nw7EFBatSQR5PQl1t1yJAAYpW+mQ6MxGpbWciOZwVw0jiZg6zk2gZHDH7ErKPWU0loZg+shm7eW0R\npPfavW3mrefB6Vn9heb39GxqORhi1Hh68UCcwt1fyl5/9Scn4QkuFvNkrZnl9V6wtmezjG6GspKt\nQ1ERsIriBFNMNo1hjtxe1F2hAe2ZVJCISwPwzUWscsOb/ECv8LU3l6/VXxxJrlef2ai+T9+lPweE\nTVy3evUg9vH7fv0MTf1SttfB5d71KX+JJkF7Ut936O8iuxp+x8u/X3uDfYHYf/1Qy4fP3TyyAfyM\ng1NsB9E6EdyyNFvCDdiBQ7iFmgAgATEJDXw5AfmeMxhyUpC3GQ7aiqy0dNAsucFMUW+951xf6AQz\nO7GwLlcymRLJZB0wiKUzrLqZqFzul4gFKtzZa+dzp5jrZ9unJ8Cr1VoHnyGTMnU2bXzxbY6O5j76\nO9DfMumjMyJqRv0mVeJ6da7nqC4ej2+Qct7R0MCyKNCTR/QRlTc9BdiMQwacDMNsWC6V6agF5JGJ\n3XMPfEk/u9b5eGGXOM1aDXJsZgOdIrQfgvlooT13m3ehXXDSy28dLI5WbFiF6R1ukFwHW1OUIh+J\nbj/9xedzofoFvxGVHu6bytkf33/10e8MQrZN0ohjiQ5XoSEQ1TDC6cf92IDxgIwggef4fhZ34HgZ\nThLRbV5NQTGsKcBgsuXEK9IRN7VXSeVth3T9pMazLXYIuUgDgl2cDGUpIZPb0zt7wm7MqS4lVuzp\ncDMagJGTb4P48vOnHqsKT158TUdfWJncerZypCP52xh8ebcqhGtOfEQ3sUFshu445sQQxctRTpCA\nxZLWhkzKDQHP0mFUqLAGYiEJFJabJsCeq3HhT4bJjRfs3Q4jfv/74MO3R34qfjnaK2yBoW1yiKiQ\n8dblrR9coX7lgd3oYKUBhpI1do8GRzuDVquhzJZeTEPW2Fr4KcwbY+HrFwuLkX8Do0yMAQEIaNTp\nMRgGDxDLk0XHlhE8AsSeu5iAojgGc16VBg1ZCZE0FBg+hPRBI7Q7ckpfRBfMlLR0a6OFmooK6mud\neZADSTxmqhQcW9ueOu7tJfZ33y5OqpnM6sLw7ig4NZf4xFvNX3a+uH7hM1+uvI12hB2FOvq962Pv\nvycG3x+BHCIvR6AozIhCOyD4iL7dsyNEHAcgAkuUQBm0gONeB427mRYVel4UzSU5BuRui1x4caZ4\nvfWJ701bczET2T1O/PDWNzK9Qj8DIkfVnmjJ9qi20aN/60Bj6N02JBDWYCKVSIJROZ2/c98cIx2U\no5G5TvEnhr84nNCOEFuNEdBvKoEPGQzshzYOA9ezqIGARwJM0nlgHorc2x0GhbQfEDokqISUGIM7\n4wZG6Bi46cTjUMoSOo3Q62JClrKC7IGAzPUZtw+qFdXxgLckFTw+RwhJ866KWriJ5iKMoIAvj1Lq\np/9L9+7knZ+UAnB+8k729icR6N5VurF3Dt7bgZmmIq97SDF6sAbQvT7cDsmUmb3VM8C2MTBdAPlJ\nFfcxYdlbVi1LsAK32+c1cCj5xof/dw7FV1ZEnTLiZ/3xb2w8dPed04n1AQwCVdixdPTw7Wfun/k/\n96v1n831XLpF14Kq6YK1fUr5+cJjOcu+97nHhz9zoXBts7279z7fOTLeA37UxxmZBJ5qMR0IgkUU\nwoEkawQfdgANQ8uHBgHYocIYRK4pawXQ8SEPNxpBH5xl8P5oE+ZxdJxDuq09YNh+ZTvJ4OpBFcR9\nAZkg92Oz3sTkOzvUK4fFSLPu7cSlPeCB/wJubel/2Sx97rm3sg/3isknr73y2bUD7O++8GLmVThR\ncPQYEQZaJPAuUvVVO42KDVEL+3kggmgJzWOaOta1ayJp8y2CjIJeXu8LjISAp7c6r7+2de4se37E\n+V/D74Wntkbe4C5ePsq8bAwAiBkTQTKwoOHIqVu3ll5tHzsUx5MGi/ASDGac8XOHfhZIk7lXn2z8\n13f03z4N1aA73D5ELlYA4zm4iRGyRxBImrY8W4B8KOpHDBZyQLDGwO1cSYs3Db0T0NmCZDQiMSOS\nSlky2KSvVNkUq6US0fRhFkuJXgFTjrU0kdcTACbcpQTGcMPinTfaY2VjrSsGAz5GHUFFBCw2JgmL\np97daJmJ1Mf+dONv1np/9sirG7vxA+8lYBMucoxo5hJG4ERbfVo0mz5qWkOgpdmAGSire0FrhwqY\nwEe5ZARt+HA3wbcEUgPDg+R47Mq/7G7fxXa+aEcmlgc/us1kTr51IX0yCqJeuwJ7G55sisjg9Xmc\ny2SnfFJm2e1CEyzYz2x6j19YRJ5zS+/+buHIh4Xcqd5PwlZ+HskAT6ARGg18xEYMU0IxvCkHiIL6\nAgbpAEqsIXurl2DRIiUsQiqE6Qc9wxArhVQJHFnefzyAwf7V3KjZpqEUNSu7rTbmKxtrA7DgTLzf\n0A3uFoGe0xJIei9zKbhNd7pOugWBR5YptYEyTn8ci/3O3Gc+fPjEweaLgqOmSR6H0bCG2FWvLhOq\nAiXzCmXEWM7uDALf9wAsJIfJSJJL4KKKmCCOcxzB4VWu1NJYYNw2bcDErKD8xZ/udpSWuOit6GY2\naAh3DSmgBRmMZh7oxFoernn7j9Fea9Ma2Ph1ft0Hcf5x+tDWTH/2LuHpbe3H14j1CeH22zyvvf/K\nYwDqeD0b9CkahSg7MjD00AYhEZEGMJwEfZvbTfiadGt/T5xA6HTScoAnwG1rUwrAqhhYXui9fcxz\nebgwjFiqx8clPlmZFaPgYFB9tuM19dTcs3/0xMP73rPeGbNe7iNLXSVqAutcvb+RLA/2NW/+5Sfl\nxvVM9I29O1OosJnvJtAmLvYSvRg58Ps46uH5GBRYpFbqCfCxeYA6EoIBXwvRPI4QRhJ1h8yu7YZ9\nDvjgQD+2tcdpp84Rf/Oamyj9rwPIvr3WR79/grhGn/+x59pDLPrGePdwN095LEmEpkDiVncctW1Q\n8N/GyCWzOnfQHIQZuJlkflCQALNnDR34BkDoQAjCpI6rKkObjmMkzY6H9XKDtoAAoNsZCx4FHMLU\ncUKPNaGMtQkRVkoDfcDtHanGMDe2BXWS+AaliPLQrjdZ3U4r7gDYMjPkW+KAzKwe8tNbbJ13MSEr\n3K2C2r6n57tj2weLkReb2Vtr62rpB/PmwYH7UjZzVcBuwJMRI9cakvaibvKggMDmtksGkWgdheNt\nFFBtRPTaCOJNxQ7fe+a+5PnRBJnMcD0D4Bao0H8+fO/PRr7Zst5eOFy8cbC+JlFZ6QM/cI+0bgEE\n9NZ1bFqOrQ23FIHVQDViRmyU6VVdDJgNjp578PBZa/M2OIqQquFN4s2NNeJg8igONAciPA3wekyw\nHDUtQzoc17CI6+B+H0StMR+bIDKdHZTPDghxFm229umpXrnZp0GL7uSUXn1yWJv2tEKM24c2WZpx\nsizmeUDd82fBrqVqsSM79ed8L04K4ZCnDlbk7ttAIzt3XP32ids+RSQOwbtvRB4Y9Wubd+zbfXJj\ndBTebMstrs+GTbpjGX7Ti5o9SDFzHiL5cdBiM1aKCT13+/LCf1z6zuLzGzf6etdGIqacAODiYy+0\nPxp1S2++dOJrr89tH2mU0md+PPhAbPZ0Cwygu6Gwh7oIvbAryxEXuzNEbUvGcT5lge6t7Jm7nsMn\n0+Xv9MmG+ghEniDC3m2pBbl7EjCmI6FwG3JMu4jE0Qmc4JUUbBlR3ATAG9o22iRpa1CfrrY5Z4mK\nFnVmJzVU4m2AplncA7GFcrJFIMhOa5GKD0QMvAPiOQGYh7Q1bTp1Il9fDPAZzNjbeD13NbwlHxZT\nZ8ETtJk5Ovs/6y/dOjr9vQezh/ip5iMLuVG7cr0d/t9+A//vBz0fQNvvazXdyiGjZPdimglrkRZ8\n9M2Zze7kwxcwCqpGKbRvR6WIa0cVImjDJSVeQYLIoacGsAzi7bjgWQ6Qim57bK4ErHgtMRi/9Ecv\nShEfDyQj7VPlrIpGru83VDKihigReB/8kdV3LbBeBBNC8S/TkysCOfuJv26wxNKxBfvvXnTNQG8U\nSITfS7AdPTlwPBil5sfoVsT63AtRC4ZIA0vcyoSGQwrNwhZGOwhrBjC4+8+HWoV+M2dP7JQTKnli\ns8JHlJGOMnFlMtz4o98LWbDNVnlQclcy1n4j2FHHlTXhhK4p1N/9Iqea3KFbNKEzg5FmnYi2Zpcm\not1KXAqTsA07sbfkawKhYsg7ZqNoEGe6bqjM7PKzFuAD1aA9pxPheyiGoQ4VhvHUYFARIxEfyJiD\n07ZI31R6nErUCaaRhChMEwfY2hCQQY/rywGmYAMKRsTGjAySsMxEVBLWgW9TOnQ617tTu/G/kckk\n0jZKf/8jc4xPNG4HoNFXk5AgyPUt3djuCv0gcHsaeiQWDPUxwKIOZVsRu5G2fDpOA9QFUZ4Bmgfz\nEDhWZssF0e8v8w9go0ddhkQ97waI1mPdoAHoqFIb02cG04RHUN71pjFcyI3PTgGbsTOgAIuHwkoC\n1clWvBvbL0r59UjxrUEq2BccgQdsCopiR6lhDlrN6cyN7MoFfnL8+jrTbMaB46JYnNLFAZEiYMd3\nA10LW7zAOHXEBD2XQHeqdWMoGAl8LEeiJNpqQkKN6MsdIHkpNYIR9ChwiM3WIEVwAZzFXEVwAQs2\n8F0rfSH2hZ+KysGTo3uDzegf3jjTW1Ybcyc9ECBuRUY9KEQKItivMT3BFVl/V8H1CAa8bugwUD0E\nBCEN+q5s6aypQhSCUxIM5sb7KA2byqTwkwTxo23JDxUuDpuev1+ZBGjVHF9PpO5jwQ0cH95/tITv\n35WpkXUVnGOAqocaYTUDQs0Z9tYug8zHrbkI1zEa0UV4XN/kE1s5v4n3bc23Ie1I2b2yu3U4yMZ1\ngEbCIACBnlADEcHhtNWrt/GQd8lSkwR03LHOjA5v6i1XjEUpzYTz01l3MeINxyAQh+qI7yOurKAa\nwQmaZGC+RA0sLQhZ8EvgTvvU5nMvKD/LfaV3UXriQ9unE/UR/xsjZ/lPARsWddcjKQbvgBFtLJm3\nQ1fRIzriUD4QiRBB4LRP4cLkUC46XuSmeF4ALB1SFIjsY5iyRZ9avjarrwx1+ntPVysbjaSc3cM7\noAGEvQdxRbO2s4E9oDsmytksvWsz4SoOsDq+M9Y6iIuugXAFDBkilpyetRbyzaCBlvf5XZHpdTVs\nogP3Cl7TPykN+We/s48iDYBLNDZg3FyNI7m6R69UwySKVkU26PMWsFdj9DtxrFhacfvJ6mLO1BsF\nZYCT3oZ9GoQZzSRN0XVhvaulby73PzSEeXUsUpchA/zk9f+a/R6TM8Yaq4fx2y6+drgD5BOcKN7x\n439AQUbT6ZIhMWhgdPoJrH6tqNGEV9wOYrQFmjwC7Qg1ejcWIgm7yuVb62pW0zHUAjbQ/xNX76S3\njdxrEa4ZrW3d9bGV1OrRuYxMui6IwKS5lWj3BAsn3HzFubu1iizbcJ520T6IHs66703UG1Hmas51\nyUW3mwh5j4/7addFxfb6vchiRIB+liktToZOwW2nb6oghojoPJBA4AjKpDki2y2JdzfeuQtTd5Mc\nZmd6FgAzqp6QcxmDoQwZsxV5l8DQjQJQHumuA092yErCtGHF0VX3vbn7E4U+OVQHHkWaIPJge1mb\nPn0zD4m3BesL/CtHXj65/58fevqnj2AGAFoI7wxZkK9mFpmmVDZ7IzoR6+K9+I4BKJsZ5PyI6roO\nXuEjZFu3Y7jpcwbjOQAcW5oxO/u8Fm6+cLhKNeFzkeRQE6/B1FgMhL1xlLiZTVbPVEioyQh7g57s\nTPlzGR8tgPWI7rCNUXNn7GyjytInPY3ycFynLJMZQddij/T8oa04eWToAjFiozo6M09EpKhR8E8C\nFAu4VlSzZIIAeV4iOtrIlmnFCVXxeRCV14pxd7KK+yrDNo68J9q71ulp2Aff5GIg2oyaObTnOo6q\n/bh87I4nz0BumaBtToVxAHe1+//utpeOHS7M7VZ+EEUfuTwFX/3SH94x/V6uDAZ0QtUtB0uH7jDe\n+4/FBw4LQ61+zOLXYzRgXR/nVIkSaNXiBjmr34J6mVBQonsRAOLV/dKqpaDhsD1tKK/sFd50FxNt\nMcLd5JoAx67mNyLU6omG1DnrRHfLbTrVaNEPzuljVcD6N3lzqMlM9THifWnl3M1AuGY2QxEiuzoc\nT3/frfrHzThlkUmzuQKtbcxZ7UnOmu8PALBdibZ9kE5RBYseipG//fP/9F6AdyCHU0DMmTXtqJvo\n4R2HxFYIKcFhfiGHeSMjGLBFM0640fxw0tu96x8+/8kEp9aAY4CC4JPg+/nIc98N9j97ma791R/e\nyanSkZmXF9/7OFf+teo0sIiamYjisJFOnbl76A7qR99Y7fqeFmnGjQggNd7roOJANoIoHNP8ZgSi\nangkHERtB3hJMnp6rBK20HlFLXEjiYZf65XnLlYhxQAb4v58JCNGrvqNyUXjMiGp1WsIYNciVJ8C\noX7QG3aUnt61xmjxQ7OiIx9mSpTU8KIA1Tuf2IIGgWGIHj0v8/3GsC1//4mxeLTXHACf4uTAwwmN\noGQEom8TzpePYkdpPmhQFNDZfnzg3BzG60lspAkaM1F+uFU526eE7WGg47Bq87jqqpkPFIh6xK8g\niBUSiNZCLZCoDx36JnHxsz+E5tp/f/8Lb/39b3TOy/WF8Oy/WCrwul6uKwAlGo3kiANhsf6Nb0xO\nZ1WI1goeaEFdiBa7mKMkTNwUwrQehbO+glmyggC3X1qXtH2ZXJWWSln012j2qY33NbJd7PjKUbDv\nnX3pmLqHhte4SgI2If29Y6Ug8Jh2gkCBHWuONRvDPbsgREK6/FoNXJ9i2wWJ6eVYVDipANOBU1If\nHyC7mZERfN3YCFIolDVlAHUlESId3zeaQjLcy5+e7fbHMDjUCjoEWkxFzGDElu/GDlfRSDou3N/8\nafRaskXkNCBqGOz7AeTN+mQRE3QgKIOA6SJonNPBset3PPW+rY9Kt79L/F3m4gKx9vTP/9WhV6Sh\ndGz83t/Md/0WpiKy4t4apc3Jya6yuAr4jI5JugN8hKvAWlRUaoRLo2RT9oodroLRspPCwPb4zRBX\nQz9yjRwa7I2kQ8s4926RORC95bZALBP2+OGvIChtnYTKyOXryfsR1eIM1kl3gc7osDdRF/x0Zrer\nezv+XnQjl1doA+n1Yf/qiiTMhroITdDRuw92Tc589sB5LLW6CacAHyV9SHUMQJqW2saaFqlyA3uL\nRj3gAQqeRNfiFfuZNTAS1so7OyeUShalmFQAewDy0ybLdkEqgNWrN/cU6NaFmjsfYHZoA/DWmHX/\nK/y7746mPuBevxz2P/bpzTH5gc9Mf81681ngESMe0gtJ7trcmws3b9Sp952+a0jW4oSQxUDa3WAh\nyhP8mOH6nkMRvsm3wlAj4oIOuGx3Z0Auua3HyLnaSjKVvtheHov0dDh2tw8qbt/enR+mRrPJlas7\nnHbs7oMnyelh9VDZCsAxH9SZPuYT4KZeKddNaau3jOwERCXREtFjW/uuuWDjofq+nY191vWUZA3t\nG3KpHyU23T5o5okKQ7TjVB21XNcGuOlh8ACRVccaAhihZ4IFdWtveXqs69gm09UUJEj28sHmNLC9\net7p10/YFmt2HIhXPIXt5M0k2yeb4IDR6H54cfn9/9EbL8Jggxh5UNlEj3etn4fvnwK2ruiC13b8\nVpRoDsqPAu/cEsngaIvz4qCjJALMMWAncKAWlXbDpEnTGq1G+j4FijeSYooUd7F5efhq6eOlG9xj\n11BTBWiTiIKtKW9kCUIGgtc/3rCcs937j2/Rm83CHCj7gI13aiLZjky0GwocY5MCa0J+Aqk9KI3A\n6KCpKgTgGjOrf3PIH9+U0xHkFxhd4v2xOAmAwuXKYsaxAIx1opZnEVEcq/l9hhEHQAe+5ZAtnJGT\ngs+pp6Y2d2H9YfWArU06wMi2auJgRMZsz1ZZyCYkkgKuJ1spHQfe733gzF+//y+gH6ryT8vN99Xy\nbrn7Bz+j77nFbG8Dk6dEFxHrdRImRDfSaXFySewLZmlvBwFUlqn5o9W2HZdolkq5JKRLFGLFQ0oG\nIN0iYk45tm8DIjv+HaS8lb56KLWUHzPHfQ+ki73Xp26N+jCIhcTEwSJWSLwuyZjFFmMBWEgRh+p5\nW0DmBPeIG05S61KCNPuDtQgSwHsH0Lu5VnayfPXOYzsvQcfjYn9oeBgxRWyQB4WgM0iaqOGzdDQV\n+IZsA8f04LiIWiYoWWSFtiMTE35Pjpr7cszNt64wFb9hCYoN3O0RKiACKIjX8YxJoxSD6JGc4Fmy\nKoLWk59d/Tj86lNH4uf3x/N/p7X//HtuK/T/Cnx1SANEVMXo6KbDRg8XfRgt1+tKv2zs3mwlh2CA\nuUE2UNqJKIAUO6l6A5tGcJO2XTqxDzRLsLTD1No1XyRTztR86uWsH/biS3VDjIH8KpLrUytRHxqe\nPXi6MH6KbdptXgz6I8p7IMrgDNSRelvk1Wxn0nZWGczptlqJ3hzIwclu8k2eK1zYyrrmkJkCiUh8\nmF2tT/im2wBGwHShjOZ0yXxgAQBiqAkpDGX3JSgC1Kie5NJTlGsbe8sLI4cRLBFtGmO6txxvAAap\nujWKNbp7aUCx4ghPRUDQ83BOSRQBtU/87sLjn5j6M+zr4oH7j9Ra2rHCNv7ivURmwwTIDsb2mvkw\nxUyIQ0RblG1+lKbgEtrXA9CiGzbixQiSzxfR0OrQaohwRSnuwsISAPE1UQOTtYmbVnvq1NfBzddq\n7Ssp1qUiuza4jLW5gI4RAvDLNSRc//7FZ7Y4NjHe7oEhILno9cjqPorGTzhQ11/rd7t8StwfjobC\nHmzDcRIV3RSw7s0DXPQWB+0bz+clpwp2ZwAeD7j8INQBZIWwb0M6gkCIZwYC7jlA1Wi83E8cKuX6\nqy3fBMmjt+dS1E5CSQ4s4FoDLgVDBCnYeMhaGu5gaSgI8HCYrYKN5+b/vfPV/zP4/XySu/LnfbSD\nfzLa2T3/j53f3f5FwIidpor1+71EKFQ9VwU21M54csVRAQFIDMFrrucBw0ZYxbB7WZyRPBFO2e0k\nkJ8LGueBkvAPCunzSbt5I27FWGk5ZqyZq4C1EBnbaF5oUikuuK68u/j1by8Xw65xrEsmAVkGpY1I\nJ6RTcRCUe4EZcvGuI+TU6f4sbNPEpCBHCkNjTu2M33+58mKQxNezm5no2DLwmg6yhVica/qo7uG+\nwXiOG2qsk7Nh0BUwBgJ7pdykpLLTmhtJI1zSMmrN7WQBpDKCtRrjCDTAUCjjw01H822Xg5D+Wh38\n3P3MpfsgiU/mvvudznLwzx9z3iCxqc1H08+e+D7o0AmQDkgyDBQXc7mA1Zw4TCTHMd9Wga7wrqgJ\nMurYOx3XoJM0xsTsrAdBMAxiY8PJpcAm5ow3lKX5c1X/0GAfNVvClNFxA+QkbCjoT8cteQPRs0ub\nX995V6wmUbUnhwbwisvBMT6xj4ih2FBCqLXNKIFw6yv9TV9HXVR18PgtiNfQ0l7qBtzifuCLxX5I\nOyQHyiMmERqwhKmE7bAWS8IUgAAvQ82IDygJgCiMXPeSx2K9WyOb/bG42+XkMNbp7YE6RdYQSQxD\n2zKhXcuyaE+lDBhXsJwMrkztoUcY7/LBbPTyzv33cPOV7d/+6yfyg7dPnOT+WKwhNuFZCFShJvmI\n3fHyDtKF2BqcUznA6QieQUMoQDnC8BIWxLgUAfoYOwh64FZmuAm9NdXEjGOTpaHy3Oj7370quKbJ\ny1O7YDCidv1EG4GCDsgu8eXi2oiaeiXD0HFMBFArSXZShD2IzcsJywkE02mlqyAYcbZbsI9d6mqv\nuAcb6x242aal7HqXz+fgA4uO7IOh0LIkAHMAhuA4wgZuj/FsGuOYOO4CGG3rFcNuazaI4c4S7K67\nVdLX82qOpoFKuflh30SIXqDCUKiBQRe2HN123UECpNXJ8TUDTRfB7u2/pH0g/U+Xnjj8l2KmdKj8\n5i8A32OJiJ+AKxIgMR4rHE46fQTtQb0lHAKAHgvJQQjB/MjBFI47nppAAUzhLcPAwER4y0p9zEO7\nSEHwpRuh0UP06lo7tlm8TgJfRdm9A8QQ7I1mO4d6BzKH00RntmjXY2vrwKhQnVSR5f2NVXM7neci\nUHq5RSXJFXOyjSaUWU49Ib0aRuMdgqKOXsVm7zODinW4ijUAZTAEo3O1MOxm/EDwQqIf0ANKcNU2\nDBSx0Ijhq0F/YhjbhvutDOpnu9moOt7EeBCuYsXeqGmbYhDoLuxRbsa342jg81YFNIqj0fXe9LUX\n5E4x9XD/V6RvV1eRZracHp69CJwISqOjHU/sV0pJS+Vx1sAsIuExkUEcMNYyggskiVFQi6CsAAWh\nNIIEVSat0QCEGcwp68K1A2Ix2euPTHcOlL2Ik7h/PWaBrOkFj15Py+m4Upte57eG3M0QI626Bp3q\nAy+Rm+/7NShz0cIrCMcZ6PZ+mAPpXSM5irLpW6FDhlWUpCftaMfFzsKbI4NJ9Pok8IEqmN2Yprkq\n7CQ5ph8ipAt7iEE4gKHAMOVNrlCkjBajyLifI5u0G0mV48U9XvYASUdkygl9QYsgegLoCurEfN+D\nBAt1AIJ+feczOB1gj37Txp3/4f+Jl39OLDG0Ovm/PSAXB/UE7yZ3lk32sJADoTVSdVUP7XhpB7hy\nAtA2YVsxEEJDqkXGDEwe5FOyxptAP6yWD/IbrYkS73M/e/bOraFrbB732/JUyABXSc+NkLCJXD2J\nakQcMms5Omp6pBBZgwAc7+7npWHVcpkXD6zsdz2K40k5uXvAWQKw/w7js1sBKxIBl+KF6eExInqd\nVuw73iY14LYh3wVQrhCGysDWfcO0DJ3Pu6QGySDY8YwsSKF5201MpzOUuJM2aqxCNz33JEBDYIdd\nk5dDG0laKhVDcUu3m82g71Lg4cvisPjUa+3E5pmF5e3SnUH22XkLSt84/a0jOZDepDJU12EzefPC\nC2uaq2odOEaMQDPevA9qUcprwkxK1BXF3LYxrOnbENeCHdjvAHLzFtdWTex+POqu/siR5Ory+nUU\nbXF9bgGsW/MRPrsZ8nmDQljG6tgU1MDDqUab3AEZKKgNLC5ojRMnrFiVFOCUWWntok0S01ATH74w\neqRzt427pqLn33QVHszW2sdeOVOfBQGqjHY4i1T5DiTXcRdhVEaz3ZDwmAEYdOKggSd0ODSUk2FQ\n3Sje4hJ1TM2ye7sAssNiOxZpsEwDa0btMOEKPSogOYqnNLCVXDjx5WkxM7OSPLQ1LmWS8tTMFPLD\n2T/8l9fTQEskrk4OIBQc8MvNFiemQ5ev4na8MeJWABcGEEpoDoZs7wUELGWiFh0QmhdXzBRoJG2L\nsOMqUVxxICInjjU/tlno8SuhoYbAB+MtNQYbmMLWEs52jDIGuehWdG2GMfc9R8Q3w1akLTFYUbJL\ntsOSrpE1eyCPZ/uoi5W90M8m1uah8ycbajK7LWBIIr6FoYebINMndvkuXolpQ4Ok7fB6I9kC7IDv\nEkoaDA9tZdKsRgFg6XWwFhnVhLaH2RBi+S0AC3QlZbpc38TkpI6AVkd1zTHLNv22CLqJDxsf/k/U\neOv0a/nvzEwPXdIyxculO6mR5ywApFiNr5c26hFsd1q1VN1jcR/Va1GmRpAAR3owbSNQvzr/HzXu\nroOjYQSpCdFqxgopgK/MdEprW0cFfLbX90/m4LEbD3Xh9U+9NwYgkOernndZnFoWGIsLl8wYDbPS\neJf3YQMBrU6y30iiRaRukuOs2BwxHNdLQtatGEahZrEx5rXX4EEmfGNDXCErXLbf3b82dHj75gBU\n2T5uZxqmwqqMwggqh+3EYZj1AsHFwRbvyFA39HEBmdMstT/bZllcqN/M6zkBxHrtqLAXrRGIT/ok\nrPQJkO5v4S4MCy3Q/e6nOf0DGwfaT5/81jnywW9H3vvkWv/Y1bV47jP/C6SjSwhoDanY3ohCZ/pj\ngz4EgoKqYSYeB6EFW0Zql91WR/7aTrb3evEKy8gw0cwPWDB96EoUYz69czXlQ8ptoaIfI9eFKnU9\nDioSQNHb32ycrSxgySawasR+1z5QDxWyQBgPPgNg8OpjfVUdtwtJUUb6QT3hsm63LR7q5VR0dHNs\nYBoT0BaJ6OW2esbqm80GmZ75bvs0DNjqCNzr2QGBOC4Z0UQjSAGsGWQt2EIAhBcb5IEdTuGCgzda\nBfQK5WQG6myvXi9iYIDAfcUmHc4xAigAdsFHETaM1dM1hwSlF/4VbZYuGfti8+8HQ08HlfNq89fN\nfpIWb1EgKme8wArFGikXd5JUZZwsWKZFYZDB2oAgexS6I+1iUQKL19zJFitC3ZhusZWYATyNoevo\nTngZgWK3r96/k1M0wncypU0M3Q+QTo/Hgu0BNQ4CHc0vzaR3EG95pud2dnGAyPsWh1nGaiDbKSpg\nWcOM7EEexW6jFwrwm6DW1dnYFom5xRxTXNwYumxM+7vfYHxNAr1o6FBFfAxVvSjmIoZioCReCCxO\nBx5IN/0Q2+kZyYDuExGi50UhEIrVkcLJfgc4GpkIGXvUqvBCx6KGPM1WTGaT6yNEFpBPH9gybv3a\nmYfx27564t8XwzGNIq//Q/OrrZW5LFhDJQUhyQHrjrdNd8CjqV61rToukoI8YAOg9UGiRPBJzM8l\nuVHMs0mEi/SIDg0sx+OapxLYh46evK9xN6HebvFZjcA6I9xhBuiDkI9VskPTO6E9hCYS8lrHjU/k\nZGhkOAADCgrX9mQMCw+qrg5tknB7QqNOMAw4bMEClxKH3WWGosS4PWMJM5fGxoBARAqHyzQQYcD4\nlSgg+ARMIHEYoqKmWpsKYBciALJvhxMHI/Xabl2HU+3hRBTu406xjxhpE6SzrgwjlByZsuxS1DGY\nGYrN4qNyVIhWgZ1XnC+ab3b1funRubMod23kKWxnvHKotnwoBTKG6+HeTqCka8UJAFDTpBIcmYo3\nLI8FvmOjOMFyIoAisWScYnk9xXs+oFNMF0zrQmvo2fmUyrP0fbFd/p1jqf69wO29ppdNkM621/Cl\nqTETuUK+jnZ7NoE2VYeeiCJtCpD7pDS/mSvzyPJYn0WSxAjaTyCD1w29SvxfbgP/PwD6cYzqqKff\nmIYwepGK2/EqzdNryURj7n1S665npFgX92CyqaXgQVFlw6X95kAk1EYqEn74T+pHLt3Wn/irYzd+\n5zP7P/z6+DulCXnu8A7e//M/Zr82F9Egshl3B9me4JGuZ3IWofM6wF2FPfJW1ukQA6Gb9hCiKtpE\nt6D37OlBh8QS6x8t99GIEUDAJgPSpvq80z6+HXKhZHK4v/8YLk3H3mMJrj65ScK9iNed3gz0gkKy\ndPPah+pT/FoCG1hj0O5olYB8a1wNArmTNLnWy9+PNUTNFG2kmQcK7KDJW6NBGPGVMNaBP7LphIRL\naiCwRSWq88b/h1q/YJDsLhDF7d9xP3XKvd27p3vcXeIekiAJGnyBuwsLLCywwnJXgYWFJezixAgk\nxGciMxn37pmedq2uqi63c6qO2/sx7v99Psfjq+oUrRqAApoF02WptyPbOy9lb3AjxWiTMS6tkNW6\nd9uUuQBSYd2u1+2iX6DtYtEhcp2QCoM8MuByFVDRvidfBpFD59tb+1eVbcV76qq1vQhZ735CzwNb\nN1TLT9SpKopAukz5dE+Lkvl6UfUyQCks1CkWZxt2Ne8LNsuYSfm7ijLr4dQY0GWTgGTDxRy9CBtI\nC/iqGGvTLEPINsCCID+nyAuzzowSEXSK8i/1syGFlqEVHoQictFNtfB4fhxKVbEKkhTdfMHXVpHj\n3cDKUErAdvKW4PEBgQ7nOzTP4nzT1mVDBoit00AJ80Ic8lhQg8+hnKcBBXgAIRSa7QETfSVfogt2\nUvPoaef29UjnLLtW4b0rHcAv6k7h6FtjLWZO6Cfxakch2DeJCjla9mhgeOobV4nzp/6md/7ZY303\nc7v7dFK9tW10/JvySgKYJcHWK2GKlYQmZTCaGJdYulikEUbVAY0yrGk1WAlAUqBO6LYG0RbUpLx2\nAwVYu57jPUJThFmnZUPegsdAJaA4jMZCoDIXcXTKp8QaWLMAvHhdD2e5mkeMui0GzPUXQhCMB9QE\nne1lMgH7CtsKaGuBUDOrgFDD1XIkHEfcrAnVPZAfBPLdmINAJS8AJZ/VxJy0R4YdDlCihMCI6gWm\njOfiLbiNDetIQMeAfCPGN7v4ZQsvWVKPp4fHEgBpNPmRtQ2GbIb4QpWklDYz01OlQxS2FgBXu5t3\n1uOdvz8/7FVG7z9U/i1y1pdE/gvNXd+0EfCcLFBBpFGhFddlrBavIpjMQz7YdAWQEXlLaXCan7Kj\nlRxOkDXCcokQkCW/H2g1TZdpA3JJFWetmsgRgKAdjDIERwWRA7ADAlb7qi8edFLwsquoFSQA+0wp\nAIHubLyuIVZmKaPdnpO2+tiNTpjcWGtiXDgJJB20KBSFUSgkCJ6ONkAVZIZsoU3UBgC1Cd1QvBDv\n8Wmmg7uIA1XqTVUmXFyAg7mro54UZLoQ7/h62FD2mo/oNmaYBBqOAMqDNz1BoCJNnz3cJZpZSoJy\nbkE2kXAJnH/tnZr/ctvRtND/7yoaXOg+M5AbmUAK/MGbUYCS/qKCiN4o5VKK7rBonhKBFSgIEtwA\nCQ+GFbhyi2+GAzBiUMhQK5gLMR44nRUB47hR0imoIWAzChbgKMiWQVTXEIMggDLL0YSc1YVcjrBY\nlCF5GGTsfJ1j7DxQzKzJuxia8EoTtq9CWo02zpuihCly0gAI00A9SUVTmqqu4zS2iaFpGvaKqFkv\nA8b0YH4TsZWa5nFUjEYhJchpLKMmdBkudW1YnocXw7NlIa0vVjL5xAUJo3vcjJeYA3Sj5s0oYkfT\nX+/Sc6bZqYRoDvFXbb9YAQc/NXTF/+XE1IfO7/juwslLvnfHUlvEVvuNo+OhBeAoEmKoBkOYgOQl\nVNPJIqW6MKOHXRF40lU0XMdIA6sp0fZ2Haf48vYGbtQ2eRtAgcy8KVNYBeAKHok5sKHAZpPHVFJF\nAL0ZED4F0+FIl0VTgARJdJC8CDhZIROAETxqLeE4Od0iYwmYCQYgaQYbtTejXB2UZYyTSjJUxaps\n3+3+kjEUMOYUW7CZJAtcrtHUPKJHEeASaliSqWqGIRTsqK6jKK7AY7X8tkq3pDca3Jo+1ITLSTYn\nxF444oAqJhjI9MZ5djW+QjSp1ACz3ivx+SHJ9LaB/e+txUK/3pxbjT1F3XEFekCevO8kPRazvvnV\n/NMAMi1/y4At3bTzMAoDh7c8iNWkdQ/eBnJeSpOhkLmiDUk2DvnhstEpRsoALjAwMCNZknJbOOw6\nRBWiCFf3EjWUloS0XwWNsuMYnmmcE1u6jdIYXGVlPFrgxWQagFRbuc2RdNWX8ykRzGvJcjsg7KKZ\nsbw0CHpbaN4hLQTBx+STF0vuht7hRbiBoHgdBiZhIibiSLxKsQxtaDTQbQdlzSZZg1DJw+b6Ji9u\nUddo2KsM9IafCkZ8FcST2+fCQIc9uBpZpE3Uf63ZVYApOVouOu0VWLdVoG49v7U6nwwlexH3ZiGA\n3Do6q9/2ruF+5puxA+cbbgQKAs2QzUg5VGv5LYUvCDYMgwomA4YwdLrVoD3ISkJGXB8+Tqf8NTiU\na8ABQDR4zNWABVu46YGAxcOGxaAyUdMhDrCki6yHduYNE2I1y6kI9qQVCQE4lBJMEFfYKopSHFLD\nifg6swaAxPMqo3fM+lBQE4ki75FUVB8xfzp9RXG6PrXf29JbuBWAgauRjg55dF/TMBDY9LiMrjQc\nFG447Soc4RZCjW2x6UZIT9IQbLj3RIO15YYAj2sYCBhOivV1mJTgRKG6P9TknLKEz9HzYtIPTs0g\ni7HvdB287ju1qceKtvYX+APvHCgSs7vDXkDzFR3Kms2GV7YlwFVLEB2kTE3TdVEHCK4apEdsOmkH\niLAin3/7tMYjqxaPK2VgEiXC4BnLA6FAhzRXpS1Dkg1gRE0EKAzK8jUN8EGqinBqaLXuoW4VKVLH\nEAbARdaryezKjCMhJUXV5hs1yoDnZ8/j6zdBhLb6uSbthwLE7NLqX/7qoO2awCGCjL9pABTDUNWt\neETMpHEYVN2WAXlphoQFE0ZbSqCEETSjd3PEUmTadFeyhN6BXozGcjhoehXLCVTagz5h1U/IAC4y\n9ZYdTfmE1U6wQn5v5r0t6ermv3rwz8W/yj70S5YupbpCKxseedsFeJNy1/wtN6gapMZlYqiOWojH\nlBEByQDvtd5ysZREMknc4XCp+NJz/6ou1XhhtndKByTFFli7pWu4DGCAOVih5cNapuCVWBuERapB\nyjhi8gDQGcuNtcOr+RpOkF0pDRiBmhnQ6gFMcUOL9rqulKHlQRixeDwyB8oBVHZUi0F47b1Ukrue\nH+sitBaiMgU/BBzSZbiWo0IGbMGAi5g1N2RB5YioQjhskl6Uv85Q0FJjVVkjcusF+Rw+KPfJtg8A\nEcj+7jzFbhpowrgL1dY0fm+QriS4ZKwBdDif/vA1rDP1eKptIbP8anDt9J1tP094rNfZ50ATwj0o\nTuCEDEmY1E7iSNFAwwoOidUQyGyriqhgiX4/sqxKNNfdo6l2mzsHJukgAAZEQXgw6OcJDvWqOiV4\nBZTDHRexZKCRutMIFDWvocdshwo/8sGvfHRTgi9oS0EIuJRjFxSmRrj0jQApGDEPGvbB4UisVuGB\nkvVIyW7CCQoaE9uqrx/+2CBQEcOn9xoqCFi2IlqMiGAMhzQwZ/3smToGIyLrOkU0whYmD99xq9eo\nxMTuxtm+Zl+pUf3V3m53RqIAWuhbnEVhbbY1PwXCwJaGOTPSiC/5SpEVsDv6gp4agTt1pacEWwMk\n87H3vm2XHoOLzBQMPKLbCgoq1AxM92oq1anNTm8aNICG4SQJmCzVZaxwirrUHQ7ngljo4JCpybxt\nR0QWUEXMMfU6gnF6CwEIna+3O46BtwhajgBDpcp0OVZGaQ/I+LcnOw3ewuw6YVMLHaCKC6rms2JF\nxQEtVzPqe7BqEyvGm3ExDLqsfCJnl9o5hE3u7zLIES+RJpNaE1+LuqBJkQpwzLCJaDWWbpRe/7/v\nHwrWFBdyHBa1tcDRxhRGmdoMzBYpLUlQfHnb2Mt6N5EGSkgVMK7RXb6wdiNOVYjNMmFyptoFwxkE\n2FNemtg+9e6mrT9HHnQuSdSdN8D6kf9uDqeHEtcMDyTxJcJmarEcRSGr2eeq3RncW7CRHA684zvZ\n8qDFlJCWYSfUbKGzlIQYH6HmuteB5DdsR4JpTcQoQkcBLKiYTAEMmJgNpDGphYaMkOyarjM0xk9c\nfcgiVZpJKwNZEFNIq4LiVZR6s5NiQ5ChyBBcB1U5Tq+Bstmkmm43I9MlbtA30U41ZL5gefAWV3dB\n0SfjCgk1cAXySj5Q/X3/gTYX5uoAAR50rbqh1R1LrTKDeHFZ3obGlzyCo7y3iVizI6DLsg11jTRb\np+b7I5PFYakti+U9ne/hEI+BI7cq+oaVan/vC187+lFhJ5vz6WduCz3Q/813Ru/4YdPj4BW+ZQFL\n4GAkAF3+Rf+0x/ZAuCw0wfgYNM4ZUBpzi2GvvF5AG94yjRfC5b4SByDYpAyXVHDTBpqBqS5hS6RG\nxfJSuApCMx4dcdGy19G05J4IDu05/ILOIT6p3QoCTXSxtnp7YOGKGwrmTA+Tjjnrhu0IYKEDkKoP\nIFBtPWg5WnCB0gtUA4JtrWXYpg3CjgYRFAxMFKPpoB792wESr8MQyTCSgsb7JciQlJp1x2SB8Zfi\ntjU66+kD09Wuug4k23FbfqmseI5tVFZ6vXSKJBHvOkW2bBPEno+9hZIH8qXClx6rs5SW/PeRjy3x\nL70jdNxAAcCqXqwRrPMAcjAaR+y/aAVblOXXa4QF/O0V3utWMEBKToF06W0ousYhrNVb7ZkHOEzY\nVUbESdMxebRJYQ3EWxPqTR4nLCCFRZ8j4UKr0TT5+RHuW+ithSxJW1GFN4C6odjEBiHFibC65ouL\nBQHKE7InoK/cNg4cP5vBbZUwuZZh++RqYIEjLVT0ZSMgADAHs1iDAChqV6IFeKjNgZsAY1wJmAaq\n6IPTUyN093R+fc/sLqNyobcQK62N93UbAQxAkVyTV5no5eprp5WtfVvAFOf0VDBd6Wi54FfBrlF4\nf+db8id/iYofutD+JnHwl67H76Kfq4SAW/MYLc7yFzCMznW4ct+Q7XjoKqwnZAR0Xe1z014YsQxY\n9WuhCGTjO04ClKr70nFgm5odILAWyhddu+JxIZN0hTrhSNG8BrCWgK0HbENlpcAlRejOYG/MeDGn\nIeImAcxZWsjXmuH4JAJnXMNuICrBELpu0JchINcTTUIPlji5ZSgqksirDBZolsnOEuCAS1RhnGsI\nKik6eY+kOYgEu2gFwl3CRXG9EqBJd7Wa2X95/wp7vagEC76eZGNyNHIW4MtOX7a96W6CUJ+0gckh\nti+3RnanGqLkATdjrXj7TLqr6889m57Dh7N9A0av/ZuT+4nr2hpoBaCobHIuZ9k1RlxH41Eib6gQ\n6dR1BJSjmtiN3hAaMSag04xNoew1woEC624kD0xE9UNFDpVdGGolxUDFq7VUVibRMuoFSr+keSqx\noiFjjkd8NqaUDDKKUjUfjKmgYzJgDuAtaaW9mOqgqWix2DVLoJCN6p23gG37OLSlImI8AxCv5PDm\nWLVss6wVnVJA0+uvijCiIiZE0A5agzCAoJC34DFNCyVQLdicojzZ0VrnXHbhu9/pKe43iWDlQK7A\ngnrXusJlDWqgcwzztqCrpHahr+EQSMghcPCxfxjpe2NMwM4Xy+P7ry2Gpg69Rlq7jxanJfgqiFk1\n0aRcg3dECEdpAJcjGc52dJTCJABXGLZGcLkeNSoHy7ZDUucQ21cLNZYjUQDDPtf0iZinBaGWitt9\neaLF5P26kazTwC8xK/EmgFEOQiEcyxaaSAAz4AYj9VtA7FkVGhu2LAmNINq4a8yA9PEg2mCAalbj\nwAjm8YBuc6FCl9eokgxKp7ycZUuqHDCBZTIUJENI06UVDCJNA9C2ASwal00H1SgRh3U+5/W6Vyjf\n7Jduw8iWDNXCBltNAqrUFO0gXelld6hGRUo2WJsIufkoPN6hgtcfed+LbdWEGzWzA7XaDlKOBieh\nm5jYxn7tP1dQkcJkruXJ0WgppMgRkcvQCu6xdbPiAVkPZMaypUg6WeQmWjGTLiSX2Ia3AHE6CoiW\n5ZIyjEKu7NdDdVnx6U0VVXlBIsoAymO0jmWB4UPNSN2keuwiU1/vssSo6AUSBluhdcUvE2Q28Njw\nN1LRVFltq1DdpjQFWEOOTvhJO0ciLb1JtUCLrDB+QxGxJg9Ip4XasEdzSReFLNOzEiJFBwIqikIO\nCpAcgQOR3oJNDrlqx4MIPZqj6AKdGtTmgMvW3N2FrDlxINPKd0lUu1syqXw3Yx5b4UD4vGLtiF/Y\n2fXdJE7Rp+5und6vh5CO32tt/3YO6Lq3wllYWNXcAEkEyug6raAmUotPJMugD2hkzS/Uh+wZnqDr\n/iZb9bPB2opQjBWBniw6ts8gC3RQiBPjqxtdvcVAQFVdyAPaMcWACYeVoLa6ypmoqBt0vs3xI2bR\nBQClkGqc960E19rj0ZsDy8scYHWYFtFM95USyYhRBRIFJOcnYZ122ysCarQgGsNcQDZxAGuwwTU5\nUoRYOY40UNbUBctGXThrdjtCaMUoXELBTvhrbNBpcri9w441mxSwHDyRqtEKNFV0iCoT9yjD7Rxb\nrRZWnRYI7fEHtn1fqq2O+E+Mj/69sSP0c2AV/hGjce5bQKZcxsfY6xCOmq1q0Xa6o7ZAOF4jggnA\ns5pWm47B1xW9OF+QwrRCCYUa7mXiZhmoVQi3ZFnDaBSn5597/kaO5myedRmOs8CKwbgpJCw6nFtV\nTF4hvTyZrIFIy9lTAl7U52oTZcNb4SmlvHJlHUbYDgQLcVKfBgQ/aeKGw9UIkC6ZVsPKN1sNUdL9\nNooCi6Yc03R5l3AQr47TOkRRCma2INKRYX9bWk1LgQCIUkFrgIuuNSspcUZMm6JFAUMPWJ0JRCjP\nr5bwhi4v9JaLRt3OWjiKgWfy15Z+M4iUb8QvHv4ceGOwv621cGLP+qGR4votEGBgttWCDQIkgl6Y\n4lFrHW/atmr4bRNkGCEZh+o0Fh7kNo2MSOV0E+5yNHveDejAjzetOsA1S0GMGz/9Q2QwphK4aFKy\nqtRBsQaIKAp7QWWmWlIVAyDJSonSpYRb9oEWpfvbPZhskOWV5ttv36CKEX9FjaaqesULgq6adGMy\nOWD6YBryCAgiaLLNGU4YxQBMAp1BDFERdUenWy3MdAq6RqGqZRKwe1GIIPgQqOQzxGvFa9f1eRfX\naMqReneuAsLXqNwo82E6mVrKprGGr9AsVTwoQgVQEjwSiIR/E1pZ6q7/t+dicfbqhVci++/84o5f\nOjc+MA0slV5SYUdT6KwFOzbeaDk0brlekzGrgMsCuiBBsGdCH+pnCazhpTM1D0DYcCYKjDJfC5AQ\nQZiybvX/7z8OEc0mpGMK4aEBCFJmU282FLhYwAmzDiEGjvcS7qpjwzroyma4yeRc1RFYhl3D6Wof\nDnJ9hSAOi3VQgRVJrpG0jprJPFxYlwKmTaxjBqlCDEBsR0d8mNUSEEWBDUJTDD/RggCkGjwaMIur\nSeDIXcbVfytCHYl9Xd6avXhpsGO27ANWgSKArXqXa0GYlut8KqJxqT7Sky9GEACe+Cb3u1d85nzh\nb7YSby3w39vjib14x7ZfLux6Yft7uJrxxvG0v6mAhp+2U35F4amm4vAi4wfpdqJGakIdpjv8DB+9\n6tI6zTVlM5iyUSDyTkDCVdxCIo3EMb9FQEmt6iFNXcZigISrCceqIaZHsWk4XiGljoJsJRwcK/pA\nuc1jbFb9y7RMNaq1It9GcXn6lq8gUEgTyLFmwcsgtI1LRkTxCWI1OEeiILJG6TAQoXDMhjCZNzDX\nU+cd2IZdHIUcRmZNWIXE4bzqhouifu8Hj9zfE6udXc8OCa0FsYQApy676wpvDPpBJ9RPYVJd0hnb\nbPrCqy7AXtjT9ceT3QdTSwc9fdDu1BMLm/8+q08MnBhDpkCJ97GFlJxxdcbrmiqiG0k9T7GFVt5y\nAVlXGmhQhTRvc3KpcOIqlw8rsGtaa61kBZA6ApEmgqs6wEWi3DTJoptkZAKB2RbQDD5vNFmTiHgT\nUnk5y3IA0ujSSr0C+4HkZgngrGMWYoT8Qi8ZSc97mQ3itNRa84FB3ZMUPKBUVm0cC/tauK4wayG2\nwrT1FAHKcJYhEozl2IZi11UCR2G5VYdNRrFhb3qsmdeRWoRLCp0b8Ni58BZ6M9NrMi6NgzKOsWHg\nyilPh93WbNS6JHWrJzrjM+pdDqDp6nTyvu6Xwd1XF/5wo1aM9M4kk/BV9aFrUwygHF20WSJGQiVI\ngWmCcVyTg1wf1OHVAaNw9YajoP56sb6agTZqeslXt4P9pM/GAeWwdRXBKW9HFYUkuVVJQeYq4ctr\nHoMBKI8giAVoTIZ1ZrdnQIeXAjDCRg3vQA14ADJZygYcIgLROsg6jYC9Js6Vh0CxDwC5AVijBdMe\nVzR8qUzRQasQiZRhVa0hwDEyphtwFY6kaULwW7oIKSpNi2bT48JTdbrWyYupZkmTp1VajszcTM8u\nz4SykiaDsL/+puGt0dEUWyryPttVI1ft2sACYUALoPMSRXvi6TL5ZnWG3VedvJmVYji5DfZzhxHg\nuCjOQBZd4/iqKOpmE6sTml5zBEVUgdvDtbOtWKJJuBvRyGq9iUtiU18ojNG2BWQmzft0j0jo/gqi\neWmWxDRKVMKoaiMAX8LwPGSE6nW1wa25ukOkWc2sqFF6XAWVxZYfobRExZCdWjbR49M7u6xwl+BN\nVAwAK6lV2tBcm6SA3M+htMV4fFUF5lS3BigUAaRaJCTXxVwJpsl2x/UAX0jgZQzG+7I3UR91d0Uw\nEqG8Vutsp/yN7iCIkxoPfCAhBByrvmA3uoSWaVYoA/JjzaDNGx7w4JdeaPvVDaWTinSGpKhvpO8L\nmNvxp2livizWgOISdROwaazFumgSgVDCKZBEgDXqKAdobWW1ZC1VkJAgxpoDTrzd6GKkzlLFaXUB\nEwQaCpIjgGZaVshCdIU0YcGCfTSCgSbuoqRVylpjlCp4LQMUEnWO5kOOTwiAtqS/wqmM3jmuZ0ia\nqDX0pQaTXy0sOKgNakyv6QLD0WQIyBXNKztMnvH30w2pHAeMLFOQEUQwVG00UQ3WqhAjKzZUN3nz\n//Ub+P8+aC8++6kzuTtePaJbm9yKMmKNB68ehuaUnQutvr8/sSz5d5w2kkV6/TYwkahzvox3na1t\nXc31rH3xGR9BLXRc2Tl+fae00y4Tt9pqnassTLVfHV578svK5qrY/gZjXPnQDz2fLK/e3DYYUCpK\noSPC3/z1Vy77B5e07Qe/EoNnHmttf1HnK2xmILP+0CkOevMjRv8qzXW8t3OBHMopq90qkfD/54eZ\nk/uMjZe//HcQzTSxsoetCugKS+NIKdZyMMt1Qhn2r29P7TDSJH/0dbrFdK/VPD655stuOsM3BxTz\n9b++/uD49vFW7+z6U3+5xX86dPuqdkPb0vfTx38tPPiFv/ZWWeB1dMkKGa5ueByCzGJBZpWyqCa8\n/b6/o9zNNe6dPV3PTzzjKzx9aaG4/geldfXRuTIopDsHf79LbPTRqHRpFW5Gjw9rJYEukfcmBoBS\nJCq+ha53uzvZNmFyTjggRjzDbbBRbusMggfv/d/Sr9+7fdfInql/+bz7FP4E56UXJu2+yfEzNIiD\ny0b9yeyLm6WRf2nmfrLr4roy0BFg76T/o+s6QDpm49vmiA7zsc0nguDeifnYofynf/WUzc+engKq\nogBECrl1xnHC7RAKeSwccxkVYLYXyGNNuE1rvabj9uBinbRnxY4eROrpCa2bfsBt9TSmiFRipO/5\njoww41suXIfk3DOfIbDPXQRkrQdHJMvrEyCEQQM0YkN+I9cI023wALx84oXLu1Yu4P92IfXQzo8X\nnw6O7DgEb4k9QC33bwX0ZmCMLfgzNH4URoJyr7yvNtPBaZnr1VUabNWna+sQB7udG4OrwaRU8/ao\npcBW6Hn69Dg4PvlV8zAGv1JLjjqrl2/LRa1wE3Mi3T3h6F6wrHxMktLGtY/vuP5ttXHH84d8K0yT\nF7Jnfjz7YTCU3l7/3JH6CeH0SscKefn2jxf/6J7q3P3ktbOLKCB4TrLgeg0YTFM0EADPmsAyMlAj\nD5fBTl1W3VG2s73iu1IGYcXLSAo0S0i1RkcP6L/6ys53xfcPrsvzBFl6FL+cy+09wnUsPfPF3/eA\nMb6I2J5ms6VKWl0DCm43GyRTFK2bSh6+AB14/+nqBwZfmMlMLlQ7Hr9vE3zLcyzxo9LoxdNAaWol\nebxxzKj+KeefTl18Bkr3ly4fNYfmS5PgnWB7bGxD12BQLeYmM97F9ey61TYx1XgYy4bA74TXyMGM\nu2frxhtZ2/t/rt+M3NSOBU+/yVuaBA4JjOPxbBDefLn6e2tzQTJGt6bsrigcprfPgOzwxb0/mLI+\nfyO4/T5PHZkt3JFZuBuafDf02UdEwJEIrVk0I9A1L78s1sv73HXK6SZYKQSDBYuAy6ttqfZoeiPp\nu7nMyW1Q0rfk3zDI/Rq8/ETQ85Fl/GeVN1RydIAOr9KEuSzWm9bFuy6CSZcgYdsu0KTJIOEi0GHI\nz5l+x/X5XLin7bvnb28XVHcVK4/unl4531Sy1cSF0cDJL0JAFLGr1LbgIhIfU8X+sHfruKF5dhTC\nehI3AeFZrVXnCjV5QPTupRa27Q+1Ko1lfvOwGSbBr4Afb/TO4utn7/De6Eh/d9fb+C9+6OpBqN75\nQ/CjeoW+c/6trTm25+qWq/aDiVpgofBT75RnFVTB7mtHjcWFajF6QrqwYctjXR2ntu0GrY+7YHG8\nB5jlmgaC9VjTEBWmM+4Ey6AdYlYliCu6QDYEfRCRyWVk8/VkqjfWqM1afHCw1FpOPQL2nyUx6bYT\n7buPJzbm8xT7Bd9nzHV68/P9D5p+gEB42UpTftW0HSuL1fOpvENzUMT25gC8zfw/RCFzY3/j8B2d\ngZO7PuGBKl8I/7vg8T4Hfg20sLg/QYb1yeVSYLFZI7qEsK9VWxEHXmO9YHhlp2dOy27JvVNXtU1P\nRMN9Pod9GOOv2htI8MPz0/6my4zzdHrlIehdpvho+cefxA5sTZEXvIAZPDNAWB4e+ag7/1xfZTt3\n5fYnHiVuHJrpWrkJfgtdTtYHI7PKY7bzD0itm3sfnA/tajXfqpXiAIYsDTMTeRP1FAsreTIrm1a+\n6NI6xjlAt5L2Fb4pOHAVzS8tZpbjE+dza4nsjNe+CX6oZM+euuYGkPfAl9bcTwQm4eWVA7Hik+vP\n/uMwCCoNKmDjUp1INi1JyeumX7NangreDMXhhczlam5/8Acjyf09z6I3/1Bfjj7/5034zt33Tz4A\nfHI8TaqrAu/rUTeOJB4P76MMPy6OzGwOMEBLgAxlClPMwUJDwU+dv5pxiQW5vgSRZwugWFTXfJW1\n+i2pd4e862CweYeXApJ3SbOjXcAhNr8GvSqL8ezWxQcMYvrE39au5Fj/m3bX6nfBxmPylM8fHsIu\nbAn/JWo69j9Ln55qNLZ++WgUBjAJCKNV6SRTJOfitkKxaJWwaKcNqbKgj2lOHzg1qGk1Ts20iYnd\nWidby69F4EBJB8c2d65/pIe9Hl7kPvA/nsv/LW079n5mvqMnuvW+OGjyXBMfxj1uSyFEzPIHw4pT\noEotCW6l4UfIFipGOz9dN5+Kf+IQdteOsS64twbNXZi/egXs7U3Qumev2yUVjPyVxUsTJ1f7t2a5\ntIWfaYBGUWS7RKfgF4/FLiwG6pdfeu29AQtDhIu4B3iEzdDmLUuPxyPn3OBc/uRdt0IXjPiAd0Px\nAAOIpaF3/nVDVh4ITX/0rSM7D35wqbRFxEkLPe0pA3fikHl28pJB7Tq+lTWdN9SvbHuGODimvHFU\nLALCa9sezVqBmKLM4f5ojENJzWdzJbQTBWVY3LV2d9mtVAxsW8fhw6NwZ0fEn5fC7l4e3A7Rj32F\nEWOND0WxvwP0Z28zT586q/sZbYn9IVBdBTU0uE4TsMLBsXiHQLD9ihsARDUH51tt8G3yO/pYxLlY\neuPOW4Me/+6HnfDZxEMPPAjE9bpeXJ/OyV23FBJrzZ0rwjcWakXcVtoCIOV5q3s+GOrgsydPWLUK\nvTWc7ZwtI6Ac8THgoHLpyp8DX82uL+51Yhfbvu3jPwsvfjgvtN32HRgYSPOvP/jhB8rfxg5ejuID\nKvqeATY8cf3IfZs23QvGPAvaEw/vhTG26y0p5/3SyzIdM+vlybt+cRcKHDrMYjoRqeKmrXowqNjU\nKAGmWkBuSSDcJMEat1xha1clM77j4LEvHyBwgfXTVh6Ax/+EnPr6LOUhnj6Nsw28MRs8uPFD+1Ln\nN74aOQCA1/IsiDZQMUjTOUFt2BSTAkiBtVAcvuZe7Zqyw9fWid0b6+2WMYUubb+5ofT+Bm6Ewby6\nuisIKdSl3N3VpTTR6yUbFaML0SFrlwhGRDeT3povV/peO37f4RBEhdt3bUNUYG7BusHSA9J2/8o7\nK3vukI3ioebs78UTQ/etrIiauH0ruPA+6MJLf5NX/uLK1YU29MRyz669E3J1tPhic9vbQE5z9GTP\nemiwtgyWmji9f5Zw1jszwg/UF5rAqFByKeRpgSaJGKyBYZYGGAzFBA5CAfB3BMPFFbLk7Yyi+28f\nxmbeTXdzMW9fraqAbz/+Z3163q7PH96ZVNUbC/Kz71TPP9NR8Q1bPwZurpFlQB4PKQsEi5RaTgFq\nMRmcE3O+zbAtNE91vsrh2/J/Bo22qQT/2o53xTPZ87eeWZ4HzoDbaAghYmP++WT7hk2qM9rGp2Q4\n3yvNJ0CbFkvuOMsOuG83H71xfWFIhz01rH+F8d1wboKK/oET0bGxg+T/Kt6r4gOzH9xWaL1eTo7n\n5Kuz4Fuawnz6Dgcf/yp+oO9M79Dr5iuhm6eorn+I/+EEkHtWV+XXiY1kdbbOD189k+xZirRlMPG+\nVn8KkLQZZAw2h+Ga6m1sUFRIIBzT9NelGgyu2zflSOxOss2AkOiq67n0ejq0hiDLmQjiAc1XN8Ht\nW4Jtj8xucQ5teweHHqQXDn2jPfliZfFvAW2jXERh/TwDCzhZbhXwlKNxJob5IRcOmvvl8m03BrPn\n+64Mfpyh3t4qt93bHEYPWrky+BPujJ83KeY42lhvH8lFw+K1Umt9zFOy5klwte45C/aMF+eQQ8zB\nDX1QLn9gTzbU3Wp40zQg3MBnI3/WLrmf6EjdTWjYr987Ad1cZtaO7OvSwMs/rXa9cC7Z8v7Zx6yS\n9GxHHrnzy7J46sQfbrZA+Y722wcvQTdd6rHD+/T333Fp9cTbK1bAcxPsyoFaswb5nLLfi7MxGM3D\n7Yito2WyEYJhGfidAly5pSNMFNSbfOzyhXHcsi7N+hdNqg2giZfmdaEzfvr2d/tmnnviyOG0i+k7\nOwYO9FAwcMNhD2U6migPp0254VpGNOHAsqnXWhqsEF6P9tKTH1jY8d3np35rXb4z9PbIrzZTba+9\nD3LAx5dAVyRI5w29HxXq7NuxlF8P5PqL+SNdF4HP7YDdpa7ofLNzewpybrgtDG8/blz3SttE4P/Z\n5OlL3NuPPO8smIvXX77Er2wL/v0dC4O3XuwVwKYHeiYT9wbjc9Yl6IfZS/bGZPh7f28uP1IcP3Q3\nIH+dW2YPaIlf9E2unROW6vS5pwKeqy9ufLSa+zxAeabs+l2E9GjlKp5tZgQ/yyeL1DJO8iC0FmoF\n6jyOGqaRXXzhyvxYiMuiOXBAWjsHTmt/tTXzACb+t9VwW97lE9G1IuP8+zdvhnd3m4CWEE2K4j5C\nSccdfFOyjQu4Mb/fg8d8dXidqB6JP1b8ydG5b31eGmS2FGNvvPtzIE2lnvUMALyg+fo1tbY5NhjC\n2uhN1y9O1DX/720wm/QAqn3V7wm1wL4RtNrLGvuwwZXpKVc43F5wKdD4JKbd5tv52tELzcqp+zo+\npw1O/+l/r71vZ2UXCcBuaur/mGcb4hbfX0qfTrQDHdb23v6lgw+u/fBGCoh3bdvOeozlz1w9tG37\nUvW/9H3vHJ5uf2LxVfWcDXANYVGXg4RWNBko4w7hITBLJJaDIikDgaEKvXdU9PkUkLz1m79VolIR\nGeHqMzVPBFB9kYW7m6+stjVGY+roMLGFFVqzVbj9zxOLEtBbrRhi4kU00kVHvLQnSKKkTVorkCxD\nMH0hvCwxL2XGuWB0eGrM5H7yUuW1lzaEh8i7VFBs76vXLbO9xp690Xy3Iz90l+2s0AzvR24GAKnY\nYXJ2RoO8I+VmUzpdag65M40BSm8zIqAUelto7rnZ+/bRkfhj0sN/+DozkHwiMUMenTBWwAsJIdOz\nXa0uQ7nlA/RRV35SLck/LfzsCHIfAx4oN1533j7sq+7P/zrbUb9zb1fh9L6ecRo9UDkHJE821RBZ\nLufUq3XeVTw2UpVxT4Sp1jhQscpDmVJSr9Pj8Verb7XmcoMVosRV3eRqGozTvxmbHH9i5d718Eu9\n45vbv+Gua08y+x99mNy8ABSVKVsyDNYprGy02XqLdytoOEL6UFiBmx/Jkac/8OGHsW8kKqoyoaHd\nXxg4/oGr6PYHf5wFlY2VVk4qO/PzfHe5rUWgyfvjC+tIOy10+EB3pd9udG3b5meLQunijVUoPl3a\n7oFudEcDK+DQ4rHM7949+lqH6LD2nS/s+hlIypPL514/faV9FDQ6mHNy1Xp8d/bfQ9X6Dz7V8aMt\n8z1Dmd89d/rGDXBqQ+Sk2g2p1y4vb7vzRPCet/+naGf6gtcODD4QAV2VTj+g1yFSCPAJvEkBvdKU\nFKdZTAIZlON0YZUo1AWxMld/MxunQW4bUiH6B1uRbeC7SPvax9oW23F3PJGFXpBC7ULrK9LS06/n\nsocA2UYREllwRuCCV7nwwslX63o+7TSDNqGY8LHSxLmhn6+//N6Zsy08tRa6sr/nxm7J9b9d+8Kn\nQXzGPNddS2QTI35x/MxKzbNP53ZziJicoUVwxj+xOIfF01oBQsfxI491dy7VXwWv+WtmCQGyfX1l\nZwzrD5YKo5lzndIw3da/wcfvf/TJYgXwf0S6u84H5EjXA7XUwdhv//WhuDvHS+8Pssc+Bs6+vbKD\n3/hX6duykdWlz8393XC4ZyUxt1JYe/nmGqjiBVX2k4xfgMFimU2McXDSojQTVSAUYEQZQa5mLKHK\nwZuJ3lh/d/CC2ZtR3wu0x8Br5mzpNxM7Nq7lxMmouX1kgfRCnffzOpmHIUCAYp1ltOyNhruWObFC\nnzgb6eYgXofEWD9MP713WD239hhwl3x99xy8vtu9W7FTkZO5cOU4sAvk7frw96g2CVFKAQG7dt3X\ns6knOogK1jVAhNgbW9astUEOm6sIsdL2DR/3Mkh8YrY0HgXLaefj6LK5Ns1Y69SocsFU/eqJKxvG\njl+droHs5HM+cmRt6nc/3NawjUh1U+PlTzegHcbujWdbwDfpwzNv375sbiATt1bv78zjmccS6r73\n8UhgHyihHMyWjUrWsEqcFImwPq8suBbtpR0SRNAgIgfZQGbz0cf739eW6Ww1qSVfJMAsSQoYXt1d\nlBt/qr7SgR2x9j/1Z9yI7BvV2tSBY14J2FnGpyokzYmZYiv6qZ3JxVLIrSlUNlpehd/4+81M4Ysf\nefuTS7v73sivunc2wWduZleKXRNX3gAeyzvLpR7FmJEmW+Nn5XJjbaoacm/m4EUK9FPoGNrAGysA\nYO33bdiFRW/m7nC5uzbb2yXwQq19dhhbPrw7G69+9NU079+B1Jc8Sz+9cDC/Asi2fdRr/o7Bg7sD\n26S1I2K13vlWmrlBT6/N/Qx8iNHah/sj+6vminz1juWByJCyotBnn5k8XGoAPGsRpBGlYstTIti8\nczTFa/a85Y20MLQBChjWNRYUBIZg6qKHGfB0SXs6rEggxNKnwa3Ylk9oOxO/v1cYSi0Lt3fckyZ/\ng1ycH65EijVg+USJcgFue7yQNpJM8QP+qkY2rMCqQKC73uUjDP6z0XPFPLk5LQd+aZcW2fuW2uk2\njMn2ZN8aTXtcIVtKE2Kv9l68JDNbcwt9JQmFwJteXk5tWGmvQ9Obh26Vtpf+cLEr2HktHGIaBPhE\nyM2f20K/XvrSfOBln5UTrpzmj2iLocSVu94DQwp3Lrlw4Cq0/+qleHTFe2nnzK1G9a4LJb40vPZ8\n9/AtGt8IOp+ChksTXkVcuv9FdLBnz8VbegFwNi8WQ3Bt0RGpBtJjkOdBMdo0/TyepQFm9L45tNYt\n375WbZg3PHS55s+yMDK/oYAOXek4/ewH1p+/88masN7c96u7iqs92JPvJXMXElO5zaDZdTquhGnb\npdLZ6iW+tHvQb7bVbdXXXbBh+tCr7fwZHaNDbXSisn9swgx1fam/hnguL2wGP6h1AL0a8fhVGh2d\nwIVYPTVaoUIlJgxhYM/msYd0K9/qfnW25Wf886986U9e/2piNRU3g0DQ2q94J69vFqe610dm6Hv/\nGwl2/Ia6cN/k6OD7wO+K74QnuHf9KfVqKaKuvLDnrcqjT16d+PoPuzbuAVtogH+s680ipdx2Ij6x\ncpZhzh9pv/ByqYfycKCB1cKs06AComWL9QsnxudTpOji/vKNDghgjat3kHJuV3kqnp13qXxqpSnO\nLlaQG7m1aeDf8ti1j31/tLYedDee++71HvNGKprqbqOfqAlnAJ2JEcCB/Cz/4AOdHb6Ddz28u1pY\nWlHcHKRAH0bJBjN534J9xx/vT/XJFd+nX1n60+Ny4q2lndNvLJwdnI2cjcfJiW8Gt9L49O2C0N2+\n9p5vtGhzjzzPScr62LUEXzj9SQwbv97a7Ilga/HL25Dpjo9tOXb8E1pwYRLcVzt2i2Vm8Vr1wOvd\n7P/e11osXb2f1vps+Oj3v1rb+spW8ZXIpub8unm1M/P1f+yaSn9uw8Rd19BE5ByOf7C0Rb16HLzT\n6Rnuf/H+P+nOH/+DyzMeuAhXEcvW+GRudpRR4bCKFnis8M+fnRNA4lwIR2TvNNpKmngSEsl8whLx\nEnruaxLZ8hcDJOHpzKINcjG5Xvj8q93zDseh6jf+tSToqAoCJG3qpkhuCY+98RKhcu2uZZvwHv21\njcv3ip0ds2GtHEV7/+mfnmt2/rB+Dh94yQTPgtq84g/yasfnN9wsyh1Heu6ATi3HV9N2igIF3aEf\nprt8s4V7QQKd5z7/hG8HVSRC7f6eITBQiqdnfjjkfRA+W/Ra0/D5Mi8mVxvM8IKvB3RN7sA7re9s\nLje+ufDVCbjti4tjntSh/e2fh8l/AMLrG03CeO5qb//wwq2p3810HL17gI1NZH786PceAphiClVY\nkUlRW19Ird7a7oM8WA2hXKrQAZpkRcz5Sd7PknGYc+jesNgQg8V4YsC3G0ypEc/N8W2Wn5vsZrvf\nFw6GRy69Qc7mCXj1R4AmLBiOiCUN8tDNiODm/u0ivO5vr4sWEoHl5Q+md66YCJKbzQbeRce/OD9Q\n945VvSdWP3sb4OhcgvZrYo/wse+/8KP/+hE+MLkuCOTBlt61BAh5LTJXCqc8QXZ0XNr6mc6AVyoe\nSh2aqbctgLadnoXdvfKKPm1/Zq6ymn8idK11yjN2dI5zXLDw4CY89+629a+/9MHsffag/aHuzJts\ndJHCDw/+E2iMkJe82zf2rYidb/VORJAjunCjMAGO7Cw8kQL2OtvkFY6BifTMu+czgxCswqGaVjfN\nQAnUNgkBZ2MZDbA1n3e7ly0YvbCrEKtrZ6M5cNvs6/Ot96deb/yEn3pl/PSUgqL2oW892MuuvvNl\n0GSDEXoxgCjjORQjkudfbqUxDFVwTqen4MnDuZmJ1uVVAf0sngms4qQ9TV1t93uHd7yEAb1b9mk7\nkm3zltu1O9FKuSenzRYcrQYOGwD4jH3T1HS+/T4Xn2CvY4o6n88MX/YotKeuAbDY0l8T57trb268\na/n85jnLE7v1sQ+sgerHvB5wZHp8rv1ocXPHoeMFiW6ws+MrP7wyvhndVpn7IGD2XTw3c2aT7Gef\n88zA4oO/WzU/eVS/sdQxFV0BJR9jeR1K9fu2dtz3wb1hwXECDQ9jCUApgo5Zo25MW6s5OdkaWAhk\nuvRmMGh1A3MTUgBXdkvO3ua/fWpm7GZfz/ySj3x3+Wyl6/cvy9fjgyBbapUlDsP8PWTOk3un5if6\n/b3eFuwQLT+8a/eWh7kgekdOerp8ZXldPxtVnjtIsqvVM7GzoO24zSOnl5xo/trCf/4kP3Pm9lhH\nq2GhpMIJYAkxmVFPT/X5+Ko7NYufWb0KKUsPryxaSU0H71XoeMfnpjcT35ldbh89/bUfpZeVcxcv\nlsKFaAC8qvy2TevvsEPUrk/ps9eLT66PPn5oU0n74AoxATY8fe/men351+oK6728cPHvG17zdOsT\n98bWQ7s8IABsO0VpAbcndvj2LZGoSSaWCd0yIAL1gtd1JRFW4v5wcmaMbG/sWCqXuRKqhYUSUEEb\nCQ+G17YVnoih//ISKZHN/uXAXTfv/khx5P5V4O2FAe1pIjmziJT9TGeoEgBsizI9NALDP7r8X8rA\nlc+NE1Ma1bUaSvFPHf0yr5+3Hhi7KwOq6O0K64I3UsH49OjeS+QOIpBhtbLK3awOgTDnsc9umxc/\nO7CvLTRgUstETKDPuLhzHQmBA6HCxsx08M+zN4cdZU7/9U5pzwqkX00EnlamAeLzVaZuXPvdhuVb\n/2MsTFeNYfoJ3UPemRmjkuBm8r1gNEEffgupnhPTeynv7BGFO1Ht2jMm6AByFctrMUouV6vASkVS\n4inWxVdNqmRqoLu+K5seae2aXg4szNPCey1rba0TkFPBYhMF9k+2TR2X0vL9qQC/U6uw6pWPq698\nd0Zlcg4PAkrDadptSW2ZNpCiVm74dRt1tYqTIx14N51wUfvHqy/4Hhi3U77a9f/Gg9/09935sjd9\nHeihGw0ESnRtaKrCucth/8RxGYcSQVj0CmfAfrvZOVbcd9evjOuvyIOG7Flk2HiiG2JlkAfrFbA4\nWTLHd3QcjL00qh96IDUx4kuNNo0gpoJjo26aGdy3z6ov3r7S+YULP8uN34yvbCB+cSP+AthO7Df7\n7Cnu/ttKpS35tVh/6Vc4W6+Vg7ONfcC0eFde1ZoeimKa9SaGzMYaKEfqHhqPg5Cec30X29ZDLLiF\nNpSezjw7uoq4PSmhdACMfGeM9h/4D+O3dN/OzhBzJnv7P/V0nklKDoO/C9xw06euy6kYrdUUTki4\nJQyjPSFvPVKH/1+/gf/vg94PA0YhhJsgiTMyt25GqkYtFK41IY6rPf+p2I091x44B7WMR88b/aCm\n3nr4rAk/MA7n4++c/vteVmGLS+RoSh56puuArlzoTRRZ301CKj1w1zPFeyetNtVUxAEREdG1Dq4I\nh84dKMQXd5x98he3ug7M2HO04hOjPHZhj/yBN2b8E7eJFx55JvmPf58PQRZtsXhNQTyZqrczB5eS\nXtMpVQfAtx710QVWtnAKyKypeApeylXYHEVaCMB//pwKYyJsMkog1dmAMSSSo8wq4ggIrNsf+8Wl\nsXO3T+8D41NH9jx94O2OlT6/VpK6Qv93TNh311cEvsRmt5szQ7KFiOvhWhg1wg2qTvUuqDDZQRnA\noWMEAMs5jR5eY+02WSHYgG+xByRqyfzNDGakG38ailbXjOsPVq7x138xzQy/2AVmrBNEShOmXy0f\noel+UN55/8X63PzVmH14iAT+Aa1Ym7iAVqNnVpGE5A1aduFiYNxT8i3r4OP/OnRutCGNzt7ijenz\n6yd//w/HK/H85eWxZ0ZooLFkrWETN6ZbXmMN5bnpCNXtlEErMSbiAIgi2UAQClbglilwIUJ1kCbl\nOCoKwcCwXdbhuhu0LMhe2dHnSKfJRfm8LCtRkEGrnzjFZQVkZPnd0JWh8ZH+LD5Uqlz8J3fot4DE\nMQpmbt0Y1OQaMsNEEmEHEmANk+d9QRhZt5iGu0oS1RLTKaVTpBHSgyWoJa315wDTGVbvLcWFvR8b\nUl/sD0qxjLZ55a/6TGTlax0gmP8Q66aoRshrjLe3nQE/ee8AOgAnhrabG34P6IZvCOZHJ9um2wYL\npeCGZYBbQcD4aav1IfCL/5uNrOie1b8Zu7vqjW7btDcQ3nPPQe/Kmb/wHgGQxlkBRR3sp2qaS/J2\nlyE4CrxmaZlKGdAtvkFLVEbFZU0tzyyNn76+sAijpgtZJuAcrBYFC5xfox2xh8C7ygtJroVEAcTo\nYJK4nrndT0yxng/BJ40Gc+LpHePFDy/v+P3WD3UBjkIlubuRRK7bgcqoJXjhzsZxCxeKS7gO52yz\ngTYJGgKynGYHIJaFObnVgLzJEgA20mtXL79B6QWS3vb3q3TMY7cdmITvNQMzR8Ajg2dTb9rYTq4x\n8ekN8jb5QDjMFFFcPQlPhsBU7R1ndyy6ORtTa7JkrRKGh+jEuHwIap0CZ/pXgxfurlPn4R+cnb11\ncfwUF3svd21L+WiL/x9gcHODs25LTck25VoNOCK6voQWCi+icAV4zZarDki8gfIYBXNOsjs6Muon\nonBY4UCzC2VMLSLkeKMFyT6fiDETRYITlw23AYZi2yfe5ls3Sac5eaCdUXZ98vwG5eZnfzu2+MYg\nIPJXgLPSl5rqUAm5EJlapZZGcBfI0b1LLswH6ARheTEfznuIhu12mmoL6wo0y+s8AjLpygjdut8M\npgM/7fvRtpa5X7qwcluw+ZL1+xfA9M9ahieYSx6gel89eZwD5+fOzzSD7uumT+wGFD2QvZLKKADp\nQjo80g2R5PBiGCqtFIIIeBLpFjZcVdG0FPgKeezA0N/hwTt+jNeiz766ZzvgMsmz7R5MZCjDRKqu\n+FtXsqr27KWmjkfBXBeGe2ZpC1ZdA4vwXGxr0rEQNesrUQroqHA6Eg46sWaVdFWkobcNxB0xDW8P\nczgYHpli39+n/uP8tucy0JL12f3//fYJ888zkcXerlUg+pM6XrCHPWKwSAC0N7CCjtNsvaDnwRRM\nVhqzMW8qnXUImSLnpi/VGnkh5h+gEdsGF7f89k1P/GRX7nbPB26kbh5BL+x4Ymk8vnbX2t8+CqDP\nCa9F4R7pVU58B9k7o5uRB7xb1gtE94tUEfS6c9sNl9fjRKluXm5vY9Mc5ZT6QgRPVsGJSPp0yP94\n+yY8OLtjNT/9P7nib0e3qtvuH35GA2GGCiqiI3n61DaYqpU7nMYNA022DRKbMJCwIb7aCxFVt+Ui\nqjeJQQyKlTCsWoVZkK+0oEyz3qwlBxyuC/GiSn69qrpItp5yAV7zkRf+9+1v71jxsd5L4u/OaR92\nS/8BhJGxFAJKOtEeGmo1gXMrFuyrZZGW3I2XY1xE0gfgPAHDBYFTYxHDnMo3BAslhHpNyWKw2QCh\nC1/dNeF+QiL/XLI39X+f3tB51TrsYbxc+PU1gBd7jg3HZTI47Eb7C6qx/Y7z5A18ZvP+Ac8CWA72\nl8xhiZ13RabevyrlkiUNalYYuyoLYNd/0XQq/UrRzKDP/IDPzW04/gY//8Ik8MU2nQFrHpekppT4\nynxHRWKoCFrOFsoESqDMdREAeL3mkVDXaxBBJqrwDEOF3ZBieGmuARodlJ9Ay7EQoydRoi1oLtxM\naxVHjZJMDfT+qX2gy7vzzsyb93qyFeL6qn5d2P9vBXH66bZ9ICC5qdUqb2lB39SppThRrehzMN5S\nFa7VhF3dS3ZWKMGuBFt+f7ARqfMObU0yDVKDQL/xx6vBDdNrhbaZ1m09C9XIlW+D/MVfWdPvH3oH\nwLP56itYmAsuFCNxtpoUv9uPHoTbfz2nvfl5MMzZmaja7x709PC86UBSIAjEUdEe3jQUAdZHppJG\n4TP/upH90Te+RmQXrn5uywbPkc3WBPxUAqCZ06pyj6lojWrLpVnbb5i9pJl1LRjjAaW5rCIqTY4S\nCDBrNmQUdYEm6CVMgQFZLzQQmbWki+dOldvCsLmebuIQGoGaQR3wo0byZPfaj2cXnkPhR9/dLcfT\nsTMf22QnuP+4BcI8FeShHNWcW291eEkPGiZcRK+1wawvBjNm3aoYKzmMFVEyR23QEk0lUwlArOmi\nQOsreROyrNzR3de2egoIg0Mv1Tt37K2Hr/iaQN1fDfhWMTyLPg/vnutcbRzqFWAFdCm1x6dA/mqV\n9RiNhgoy87cixvo+u1zJTUuNG9kJL5g74bs2bf7ms+f9D//h9b4jmx21uf+Waoxa6eERMAW6bO2a\niwn6TBpMK0aR96hViF0t6wAAg4lZJuUToBDiOD4ScUsqDGo1DkedIpCsWMVTc5uZV/7zItfIZlpk\nAG2FW+UVROsG+pEhd3By6Hu1L7kDN3b4enLRLu9j584ib7a9vwPIYNFmYmQWMvkAw/HZcr2IYIVO\nMwz7CrAXn49AFIU3yhVOM3KoF4onzXK7DVEeGgzhX/zwOq/j0rshHqhTlXfkEeOBLFhxEz+8CprO\nwmx7XaiL6Y8fSMVOXW9kntNJf/92g1lTgTdo4b3GFI7UaqRWKG2w+1pVklnJFzPiBfBuZYi+fVFp\nqUH7bz7y0jL2JYX/G+tw7Dn/mQ0F0DkYjrQHBy2pjtNob6JKNpKQ61IJUdciADNkGuVEU68jvpYr\nqa6RK5B2p9KgnAAgK1UYYcxMsx3h457G/EQkumfA4ljBaC2Dcfj7k3zcvf7mm9LJlfzG8/L4/Kvf\nFc1P/2Xv/FZgVWqu2lC9Wwia3dJ+i8RC/qEEq2h1qGTDrNtdrVu4TRgBsYJ31WqrcxmbbhkpsuID\nhTfTpRIZ2mjGmKmtwGBB5CeL/xVF7hk+/eXvgdDNONTtvTKM9OOnrin2A3WzcnWNWJi7VeA9wNuB\nW+WM6q+M61zboLsSWrjCFCzYbUu2B8Cf35cXTwpH7bF46/jM6nTbYiE6EH7nDw/sFRa3Anltrj8V\nzBZkPNQotzmbmnhGUVfsGh7UzoNWnsGRMmAI3mnikfYwYgQlWJD8dNrEQTkkag6PReFIUBDmV6Y3\nMvs5VzUbOE0lgfv6sYlG2+XOJzrF7ZvOnOmt5DeEP9DFrHntqz8D/lic6vHA+CzJ+FKNEWMs7Jcz\nQ+Wo0DIFWKFhKyrSpsSLY73DIB+xbV845LaFWpEiONn18lcOiRqaBufcp8ojL4up8J1xaQ15iZ6a\nBWk43iWSznS81jJvpAOZLisc1VaxvkCpRoLVG9r82WO+4iQbie05jwQX5ham4wjnKDqOgKfWtfW2\nR+3GS5Nn1vOpKIkrvwh6PbfZU0fLNIAp31xkzQiE2utWaaV14Xr+eq2XND1cYRsBrO61sqDgbqjJ\noqCGgaacZ+CYXg54eRf0hyNMy9DLRyKEnV5TYmyHjTdwk7NYige9mbU7raePnfgl8+h1p9NaP2id\nHLqSL829KbA7QWONatlRvuIQgqusLOTOV2VvzUiutoI0ChNA5VWeiQeJjirXoIKXIRaN40pD0REf\n2M08+YHJ5fTzeDrYFRLkTy0dxWK7Vjc12z3sKDiS4bWI0k1couVyZOtet9wR03NE4lI+FN4JGr1m\nsC2HsgzCOtxI33Z+MXFq0l0RUZ/mB/ju24x88Y0/tq99cStVTR29emMH3lG5utZzuvAjEHc0Q/Bh\nOPBuVgfHr2lmlusts1XaA9+EgDXn8zVhBmoGW/UGVWyqpObSouyHWd4BuYVGOJ51saWiLHAitYmC\nS7cykkc13UITlKEHp+Xbnrb+8dL8tqEejNvwkbbLS/M54bYfaL8EZtCQxvNISMHcShFfh+D+MX9A\nNbRGBgVo02/Vt81BVQFhrbrHYfv1hl/3SJmqSUqgMnq+kz58rjfTsf1y887KldHfSs8K3ZWI6ybH\nQeEuLVEm+qSlugB7Yt5z0dQWJ7z7PSG4INeB+dwgBy0yRv5wQtOJgCLGvO9fPDXagKQGDiL/GbmP\nPT529D8fcXt/8yOBcsNdZpdxrXGp19v9gmXH2MmIyxBN8s7uk5raRlejXUXLavFsAdAYpNdoNFSX\nFBfPxQur0WHZEhmZrrIQsCOoBoINk9YouNnrNBdU2eBDOMpDUAY8/yE7e8/j310IdtCht35x2yIv\n1p94+s7hhfmPNXPPB0zFF1yDa52rBYwkiDhqgXqwKBlawCjDuLRdmtFn16m1Msy4FgW1wauQ5MQI\nGg+ATYVEnqhZwyx5gbp9JXB7rejlFvrr62/EwU1QvLyEi5HXXsNe/eOvT7x+qfwsRe2A/qjL6NBH\nloH/26NB0L+r4M4thmbpLkR73xcGExx8Z117jAEoYp4s9wbe+BdzM6b9y0sfLdLdnbNn+uGuzrtS\noNi2Wtrk7bAS0cMbB+79xFce37JlI26EYaPMDQGIJoFA1huFUsvRsHwj2m47DVWlALFeB916wZZF\nP6C0XqZmCHmy1NAd1KFAHo+A938irxS2VLp9Z4l0lH9JLulXxj+Rf+d6+T+jl8E0BDosBo6NR1wE\nSWmIt9TSsAwvIDbqh4E2EQEk7V0Zrq3PpdJEb7TfXy91anG5RYJK4lQQGq97/ffcBxPvxYbXDxSm\n//KP3329+f3eRwGyw9Y6z/GQ9gHo6/6b70wQSfR0kR7xwbA8ArakZCTb+z8L5uTNU5cXfmf6zlm9\nAXjT1P3MsgJqW/zSXOnpjU+Rs/9clX/lepLXz/zkQed9mZNf7gSRam/raiGVHWcAj7ePbNuwWciU\n0SYb89Rl0KBlycYQkyQFn4/EgyGc5DXVVGErAIEGYFwzTCeuz7UFaFWi2zxQv1TVlwtdQQwcv3OS\nfROWNn3h9vDTB6Dd869g/9Qt+dBEe+NCGACaz+cjej5ItzHewV7UoDREdZZSCcjIoYQOyPgSq3pn\nOjU8K2S8cCgrR2ZVH66aYN3LvfSv/a+8ceiFeOm1jfWTMLVNvvwtX7vvViEDnHlo7hhR8e259uFu\ndP+NJ1f4bF0g7ql1vffmRjA7t2F17x/Dun9e+49DuatT+0er3gdmy31vUEsweN37z+yzoRifvArt\n8vUf+9amzVfXv+mlQCgMz4E6ZJgRFUbB+HLJSo4GqovYCuQG9bacxQK6IngUT81tWm2Gr4wTtFzW\nXLpJO5JAAraEMJJDwEqwcxDJlvrtaDMfB5BLj4cC4ENQ4KWxaP7Vx7h/+mg6Cn227b9R1Qr4Bk8f\nRN8DlEKqthRcwS3fSMGaDeDRNXK+FeifDxDtcDqGSbO4yxQhgvVH+Ui6ZnT7DbadciAVcMKBv3j3\nFap/tXI3zTDT8t09hZ2h845MFrVVcHNorq+q9aNF9O0Ksf8x7+2wZxed+Oel5Ruj74HWJko41ZHs\nxvucfr/6h/cAzGgpf2SaFJkKeIj81XdfW81qQQl5IH6nsb9wdgb85hnlj9Op1SLQ1gAvRS2Gv/Xa\nL35x/tXpt+1WmwkT4mVRawEZx01vVnWEbjRue7xIplGtyAjNGmHSAmqLBxqYWlC7BiJ4qWEHMQiV\nHd2DhW0ETC7+ru/EH+3ntlL3Tiwy9Mxf7VWABtMnCsFgGjRTFu2KdqBVp25ePJX1D49sRJgmEu62\nayisphwhaDI2Msb7SDIpwbWI3AhWmrYpNEEA0SYeUc6cbh09BTZuuLLrtdN9adru1nWhPATueP3e\nK9NIV3Tdl3w2dmPm9Z9z3jpVoIJv+1bvAina9m7J1856cH3IB4U7M34lFGlmDxLoGgCV4dixJ7Wo\nXN3z9fKycnrLn8X6xwK017572TJAV7LBymsULo/iY7sD204aQqSGAD3JMgIAvparLWma6m8UFuYX\nJ2q1bB322oJFuy4CUASjYmI33j8yEl/OedwcyouuTZM2i4rgzPP9J7o/N/fB9Dvzdjk9KX+rQXWU\nlGdmtzB/fhr0+iF7AIMSI854ee2Fm6gsiIunEsH11VagjtLGxmkjBDoqdjw0NKfUs8PlSC2X1wOc\n6AD3nNL77Y+w8zh4a3DtSsfJuCZfEntw+RLXNMBq2Eh0Gt8fWfrgTPvE1N2RefO6NYRB7F9kF28B\nXwZ2mKi8abqPHML/F9m6rcj8kSSHX9zOBV3QVpirvhKPbJtYXWcL2773z7cnq1cjywt7/uq2kWO/\nUBA/6oGrPMZEPUwYvW1xKTZinmsrAlOrAIOVMA1o7hrmKmkqtEShWEvzZkk8WIgBToBsGFmvbmpj\n1rUAUONaRYALfm+Fxw0wgF9S7roSPctsW308l/ExpeOdTyzcc9iXZr/2OvClTcvyyPp601DHZb8T\nZNm6TvBIzxJEoUFzmqYgi9gK7u69JTmZsA5aEIKhqMxWANZqZ5lFmlp6724ozF/qu+ZrbuUuBcxB\n8pX3g4Wt4/f//sGOj166KR+/cWz91vZdreTFnpmO9ek+H0hWA1qNXg3iqTvxzO5mEilD7SRyIZb2\n5WBwFT3ym8eFmVmfU/qf7oG/OLi4bBbveG3g6scjixmAkRUthEuOpAxwhbHWZFhqLVqByWhAcgcB\nQiuNuLGYxeSGlQhEm3YQMwEOY1EIqYG1sN4YFA1JrWVUnPU76YprmT1MhrLEKDh3zJmdSUp/UZrf\n/M7Efz7Cz2wdntieQk/79p1ZBQVtQ7lcDRaxJQxuu3vjZq62em40qjdkRlFQ71IAX4w1dHzD4vFF\nzVIBWllDcaph8sUoMGOphvbOvd7R0MQ+6wXj0k++NRS6sWwNr7l7VwCzaq6RbyX/mPTwsZOlraHO\n75MqPN557SNdRAAEjUvIzuIglNsi0E57zGSQMkxhQxxTV9rBx5+K3pOZr/VCw2++4JDNmZ1rhryK\nlI6+Wk0tA8MJEYt4dKm7AS9DxxXDd8PfA1eRjNCz6gVw02RbrJ9zg3AMF4BheleaSA3WRQmzgG+x\nE3+zzWvR8aSYMxsSqWI+CGghJERC4NDCkMtWb17r9LCT2z5zWy17z1z57vvOvnTPpLYKCPqWJyo5\nVWiXvbTHE1DM6ZPBAUELc0ULRrMb1yADMmxlwrqIomyVV5fkeMWIWIxSALX8yKUd2QsAY1//9y8V\nb5OeGUbS4E6pwpbNVYDHmej+Ndl31XriyvBKR3wt6EUJYdvo9MLoNMgQ8iNT9WSxgzaajfjU3sv6\nEXieTzSMeZ0G129/ubZtDQabtNiF4U1y6umHQDIweK3yQU0cfrrqqXS1AiijM86C1jd0s9YWqzEM\nU/UW3BzQbbWLhAN+Lrgl3F0tG/MFLCwRQqmMBkwgEiI9JpJUgA22VVyNctz2Mm2zjNtwVJDZurhR\nCtOF++YrD6fZk7nH4P1tmedv/R/olW/jJ/km5lCLvl61ih827EzRKDfuzFFVE17fOINyE33TXbNh\nRQ5cUW85Uc4DSIrHIYNSExbY3UQOXuu46b5zA/qCdXDJHe74qw9Us9F8T6tnBMRuxY7z5a2zu2Y2\ngx7qn/FbG/JzXdDFRuz21QZYH3vghnHwHCkU7HqL6ra6b017uxyo3sckALi5YfTyqVil+1efofuy\nh/UObHo4e9vSZnP5bXoBeEV0th2H2yHN6Vtjxzty0ZrKB+s9FaavAEgb1gyCiDHk2ZUpiBmRGQVv\n0GrAragaoDWhZYTyUWfG77QsnveJFTKwjsvmeoQAPpRsnEe+YDZKwktou/aNZk192u45clwgntoH\nLvl9Guop+xfaIb9CTxSj3g8v4BBoD4DyPtQ0Gj3LBB7wmDrYE3F4JuUIWr5dFfPJEvhduZcr3nmj\na/u5h0qvH77/OK/+5R/uLikbNsVH/gWEap85d+poaa+rLObMOiiMCmyy2DTRdKNLByHbBDvmgRIk\n1Mh7CTovluio4DkTJ/yj62Bn/bM/zPFHn/WllpaQzb1hkOfIt+cHuBcevrgFQAKgLdCiNNOjbM+6\nZVtGdXHtwau99QIACqWQ3qaUQc2it8eOQIxNV+JZDK4Q1SCo9dcJT4mqq9wZ2I0pXBlHKCkkmmK7\n3gDdk5XIPV/d/O7DR8r6M+/e+fUvAM8nXkaLAtyW2ABGvC36LE1AXXxJr91cYnDeDNeVdSwvj1yB\nMUanuI21aE5J9sa8LkW2h7UcryHYoNMJ7njog/XY/249dXPklaXNnlrpxae/FODYcysznh9uACFx\nOrH7efaVwqZ/l10oGDoHh49v25XbvkGXzoFMVjHSCQYtqCX0/TrrIdq2XluatEiYvl4Hc63frB3x\nX9vbP3k02kltanRuNvPvfkccPDpfoYDhEyqwWKHd9jXEEyeQRENiG9snsGt6tAZCzYhmmWzAkJMK\nn1wrcVZOKHKQy7MBE7SttywjUAj72OZaZWVlsrYEwbQkBZOIRoMXjw5qr/zme+//fORC4petK59R\nN6bluAC8T/b43ga1hbTWTQXs1hKQRF8s3KTlLG12qmxHU4BLPJVjrh27pa143IDBErJUTLYRVqtH\n4QxwkZ6cWJeqt7We/9Ahau18ecn7h9iLM3vB1H9UbPDXe6g/vnRstZNbeUiqzcV93+htbny2PjKp\nJYXHQPeN+QiBRHDB6ijdqhrajsGlHg6NnFNkchRo8eXeiyurF9U1uLXn4yvmyrXnGz/77OZXXtIO\n1kFloWgxjtaQVwAya0ajGBozkfxSCLctHmTgBlmyjRm1nJPgNOwua4xD+nEVl8MQyGCir1GJauW6\nw5FMmIhRZl0lKeDoAgGGfuk/9KGnP4v/eSKi980e4xbPhzLsoDhxvX69CwRDlhGlcCgQSTZLJEu3\n1QiDwYkYaJRlmFMStu6d4WMFMb7adEpSzWczYYZft5sOmAGF2RNbNmxeHnr5xXsaZnTI+3eeanCD\nzeylMHDPzPHMSMQkJDn9IHvHmm2+tM23N3drg72jXgcFbx+DrJT7PKKfkXelKbVKpO2KsXG60lwE\n+vXCq21SYJN3x80d2WeWk/UDfwG9wCa67/jA7Brwe0j/8kqfSXg8TT+v6Q0fJ+M9W1cQpa4BlsNK\nFImyVoDA1zVII9mVKiparuIvoMCLMg7JSHlPWHSjQb/QaOMEU3BcFzNQwH1e/JJwO4DODrkHxo+d\nUh4PVLLB3xBP5nwD74C61ade8dbo9fgt3yCZxKqypCvhIo6hwY7/x23g/w9A713Z+8Yu4mR3m/76\nQM3ethRoeSwfc1IbW5A9f7f462S0ppBIOpEo3rxzYmFpb/fstszmnrelUWHsqkWBtSjZsPywrBs6\njaJ9ixZJpYJoPdm/U8FEjwgwzeYRl7EUqsKaFOeqGtpAlz/SxOgmbxAFHiJVu8VzKx6IKkFR0LLD\nP5vPX34g+Z0ug0yO76vSJ6E7V5oEsud6s/zB5zNPvYR53BwXLkCi2Ny+2q8tjmbj82UMJjs6br0v\nhxiO49I6jJoYhNY9dRwgNkLqLqKgnX+cvN554NlJ+ptv3CeeK3z58sXCY3Z56NnknvGrX3v4+mXh\nPa4ubhmPR+fXvj157gj1euLBP9d3mZVwL3zaWkDGp7rSr+oEj9+/HCU81/HpAnWvQmJHwYeRQnpT\nGC74as20+GM7/lRE/YhoThz/ZSd6Afi9qkQ7dhuhQzZwVRf3TSK2tuZjjWAKVB0F6EyIFryU0SQM\nyAqbliYByyYDPDBVHYm2KNlXslwXZmysjxMQOKi1iFgd/JC980+nfOthn7ijoSx9uKZ1PXx425zb\nMP+86yAotPQZscsATaENmExtduB6DeKQiJcXVv1AMxBadzQZAqiOaB6DAiiEtjTY1RkYvBv6SPYX\nzL98ZxZMn3ggND+9daTHjsW+FP/DUPgtsPhXX43B5kNV49Gnmu7b795lej489o+b717Z3lc4CY8Y\npTFvc3are3zkQuBXvmZVHXpzjGfPj4SjWXDHVl9mVZs7ktvnnDp2J5Amz8vu2jHqzu//0xt1sNAs\nod6GAVlFpgoTw4LXZkmMDBNzFRUCmCFbGmjqZclmubKq63XHduEyUmsyKpDQSG0WAJSOtDJ1yjYo\nmVs3RJbGsBUO0EYVmu23s76cHfHvYR9vTq/Wbm2659BuE5iAbnDEhlkzxYvhngV+Tm0EQi1iCxUv\nTZbywHVxw8uaPFBMpAlsBDJgG+FxxCIgF/RfutH72GYoS3lHtv7uA9fvDgaflUsv53v5qU+EQPno\ngNmOFEaU3x2MbN3+8TcN4XvMR4afGzrx6jUTnu5vx+K9Iyf6N9/corVJdNNkPiuC3Ukl0PIBfzUT\nwHJ7nkevBx6Zom4bFA4nju+80fkf4wdYAnQJxLqFO0sxOmVBRM1AYp6mB8IqgT4VAwTiR+EGrCIB\nuEHLKGYZpgcuw2K7XDeBt2cm79PEnNJ0ou0mgc7kSyg0CJeRIrkKDiScI2z1w9S/SOtud/LX1/1S\nbSZ46tZxZLjZCXDvMnuLaA02fIWmf8EfsFD12pzEx8n9oA2gkKVbFobCDI7BtoOojkgwtAt5URcA\n1dPWdjI69z/KRie69YvfOpcNHpT5D5ffneRe9oIT//Y16CejP1Wf2m7/eOfbrzsvPfX4Uva90BjX\n4dmP1m6R6QM8tLnnZsglfYj2xn67XAZtlUnP0ALABie3vWeZYdS+7IxdP8SlvQHjhu86EZRzO4GG\nb1nAhNXESmI9YHJ+WZJGJUNx/FRDiYIyiodrEUC4ugfIBJAwXxOhBFAzjWb/ct7uazl6CDcdD1Fi\nWx5SoTSsVeGQIAaB85YuCjMCuDPeObPl+LrsMNMW4jPveaN0xzOAYOQE24hO4IEG3bvj5tucd4wj\n3eVCzzyfBZhh8U0MNzBbxUkL0vkmDTkaadUgEoB+JF/eP8Pft/DAtTeOfmeiG23LGv5x6fAm3/UW\neOy9K9xdXX879XPZ/jbXnZV2Jd5JElOdv97+y6M6DHdsTcCNtHxFCqusWpM/xunodv84F0fhADgw\n3k3U3kdscvf07G8Gf9CYWqqPneqcffBn9PZpUKpWbNUbl/hGHyUAyc4EZnHVDC7PN3tlQAYCMlAq\npkFndV21vaQD647jYoBi1wAHefIYqCoa5bGoZrMGuWK8jPhdVTEh8NVlidXuPlUZ0eMDl0/0Zuev\np7f+AcKpOUreCCqiVFXRYrf/wo2A3eFZn12ZpvM347ESqCPAYQkXcxRGa8KMS2KKTViOxekigRIu\nmLmv+uV3tqpd/a9e2Z642b1j1mp9RjoXLwyWh9KgsTAVPP/GcWD1kenst/70JnYrvK28ufPTrz64\n4Qx8W2ZVXOdSS1ewlyUHcsSVKpU6v87sbA4vauBn92lvj749WCxea33t1rXKlN+gJ/a8ePBfP95P\nkwDEGnxP1eaYVs1d1lk7vu7anpDZFY/kPSAoa6SJmXirwVoIcDHYslua5TYI3YFB3FoOVvGIwNhQ\na6IV5woGrfSZRZhkXAIcH9hNZzvi21LWs5U/TSW/PCTtdv+jeevCsX6UBnGru629iFwo9Wy+hcm1\ncbgLS82ZuUW3ZhsAMRgZx4DrcIwFORrtJL2IJqleFDNQoP1heil4YehPu/L3jou14+c2QRlUO0YN\n/9/Js1lQsdrb3sdt1g/BxoPdW4e/0Ot5pLI7t+GnvD4VgN8S2UfbrvZ270Ie6K8uHK/0fkJsjxzY\nkkMKAAdjt8K2dscz78Zy1SP770k+SW69dT13h3tv+RU1BeiiIkl+t5bDZasbmmphapVZqtkdiKCV\nQKtl2gGLlUPAop2AXlYVlOYNk62zUAjoiifI+aurJXSt4A/IbtjvSmUzgVgNrQb862dmDl1+p7EB\n1Etdf5eggl+cuXTJA7AgQzMggS3kT2zk+pniLabsIOmWCkrClvhcQBIYYCAFWsMYGzFtgwTZcy3V\nRjGY0SwSMcGWxUOl7e2/e38ejv6N9je2sge799YkL68m9m/aAg4c37EWPoJt+ZP8+A+0n/1g9zsf\nOTWvEsevrXd+axymmbmlBU7VRqAii7c2PdjePOYrZL0XOpvAAhZvZqgrwb2ire1SJ7ub9xM7Nv7F\nyrI149ePAcWho1I9QGhoLOozEMgkgw3e0Sfzbwk0wNFWtSEBV3FFFCmjJKS1Wg0XQTkbLwIQtMsW\n6XLM0nEi1MJkClNqNUCoTsAIgMWx3vCFdPf0y8no3bu7YxeK7+wZqpUPCb8ea1JgLm6rB6851aR/\npEGKWMKKSWNWeq5zKalBwKl7bcQAFifAQXr80x+YzJpNYMko5ugAXCpwBpO8T71+RC1QaqbzPMgZ\nQ1ex5ge0QAj8/l8zi+BvuYXI5Avt6IVLa9zd47/fw01lPl+6m0N9XIaQkf2ZW4ih2Lxfns1/yD+Z\nTXuKi2M0mFmAIlw+2z77cOxsK2Yt3xgoW6+WoGDP4EtR4EfG26lKyxfi/DKKtqkGvBCuORTGdisY\nkCyA2phNaQ5fRHkdVxDMMdAqJeGqB8iYGanrbLVyqz0k1GC6FKRchxebnB5oAPESMnZ8azjYPUX+\nxgo5L7QT5EIoWjr3Wa7rEgDlsEghDL1W9noVqksJKV3LAze4PkTTvMDlqn7IdYFOwY5afnxohNRJ\n3GAtQiItEDgU51L+nO/9hX/897V34Rrdo2XzlUhlTrqyBOhXhjYg3VjPjaXEoZ/3uvD1tPqJc6XS\nk1OFr07CAbRP7eucgSrNLUY23HnjlcpGOEgNOM6dUwzYddtt21Zzec/OLe+tX7mGa2vP/KmtIfdc\ncBbuSoJqpfOaDBMqqbAcTlIBrOBrmCiFA7VoACZAmojr6BrpEDCJqq6O0UAnyyLLaICRawWnWTXr\ngZ7N212LCVcli9IAGWnZEhg1t5256+7uvVd3a+tJd33/7Q/tx0FsYkR+VQ8DXItwMFWJo3H/1VKt\nmOxtrcRXQp2rbMqAAQLjjmq6lC7jsrj1gW0+irYkSHFbvAsDWDwzP4tW6BvlT17vxbiO/T9qjH35\ng0L30vDQbhBRX8BvHqYvb4pyJ/aHQgO37X8o0lHrksJRsh9emJ1fSWGZ1TplT+XqK5XC0mmjJ3DV\ng5C9i+DNvBvYkA2tD7z1svade62Fk8p3UejIuZ1TYHsJtAUHhhha5IyAXpZjmOZHmi2hK0CZNNcG\nlJbIcRDmJWqcxbZauhvS11xC7m7XAAREM4A1CEjyH7j7A8uMF3dlT4SuKugUq0BgEizGEtVE4kGv\nqcycW8ct8m99SiZlTo2crIJAuaXAvsUGCRfxDE+ONPxJowzWgd7OrgEYomgUBy0vsIEVDmFOxbQJ\nGGrRFkUCEb2Mfqp4Wz0B7Q1DDsHPfhKJfUW7Mrxr4nIXuPjl8OvywmLotQ1fP5rZLQ3tE+rzrTs+\ndyGM/s6FIa5jRMuOOVsED+unu/0rW2/BFWyIv7IquAA++x+vD2y7zXcZpntMtebCV/J4NPFpaSfy\ng+2gAqag9aJehfNZwkPSJtQV9HJlwRfNCWuA5FlZoS2DCtV8NMSEj32qk5LszrKoAxXUEYIk18nd\nt2/8S3W9UKlbCV61g02b4IEXwEQDLvkunpz/bd/Xh/wY1HPtLd/eXqP9EytiA6y0F9Ss1dloLcya\n7a6vZegblz1aG1GzYkMAtHTRqmCcjNoQJluaREKc5lKsLisG2PPCw3f9tPrilnHrxbNu+9Ap3486\nbwVTm+Fr2ueeBdCt29Q1Cyocet/PW9I3Yr8p/fvS8AT0c+aPsfASvG8rWXBd+WZYmLjVoebWTOaI\nCbNUq69MCODRyPaRjPvuzFYyevViIPTS1S8uXizob5zAqt03QS3FpBSGC89NvvzSq6eniQhhwBLh\ng3MeWwO0W2MslAYSxLR039CH/vOBKEewkwyGOhxgjbUqGiGTdlx7tyLVlJqju4odbw7mRQx8Nrhp\nO348mZz4xzefv+48tCM907laPLk1u65+swE649fLPh8aQXxtQ2K64dZb492BkRl2yZsuAhwiMSxs\n1CCYNClCcxBTNRFGp2DCcsDZ++z/lu1dV/iN9k7zd9nbTn18adJZwhYroVcPgs5yanPPnL7t0IXo\n7zq8Ul/mJ1wklL0g9r2cXYNnawWqN7G4t5n5k/ryu8XmPxyW/lgyEVXgGi7I0VzPVaTzvkDPscPX\nhdCOfz4Wrjj2JvJ2jqVA22goQFSMxfTld//xM/f8/VwLivMslzerEjwIVCmKoIaroaptz87XN9LP\nzJZIM2HoJKgDEOoIKjV9XFr802w+U1jXJA3EYQEz7V4XQB3qma3tG+q+vxv0rg1l2fZDv2iOly6M\nvMXP7gSz8t0b5Kpa5Pt61QhUcF0CpPCVmMVUPRyAeMdymyjjILJsmjKAcNzEREe0gGOAkHdRlVqW\nmUzJ16e++peth9wzriesCj2XxqMgY5nX9CsH//Abzxvc6/BBGh0XVkjfU49cSw8to+3TAze6pOYD\na6EPnusCq5t7mZnNjTaYaNHaNXD41ay7sDV4fosszuye2ed0Ni/tLVzThl73miwQS5AfCvsLajp2\nu3Vm7VaI4R2v5IBQp6oAl7cQC3cNYAFVaRVefu58PkA0SZsyTRyYkoPTBklkkes3mlEbKI0IXQ2s\nY3lPgQbvCa9uLQQz+krnpvmH+TOFw0MrW7L6yLWlDaUrAKtHMLsER6VaRYK7eYh0HELGYGdE1wxg\nmriBYK5CNHGAW5xtAtxhXdQy2RoOLh4ZYOyJCNc43bs5dTqOvU3uffsbbzR/tv+T2TD4tmj+aOj7\n3whZN+4Q3s1e2gj/96Gtb4scM9Ke1eFaIFoSpUfKtrfjQ8nBmF69LOfWRGc9UAvcDyrI8JtJR1B/\n0Do/GRn52U5umi+QO/ewLrXaBL4qrUrwqlbB7trQ17i7rQVUKrMOAFGGXNAqtmS5qsC6TQfiAvTW\nizJNUQ7uEDZHA5xwNB7pwMlaHYlTHi/eS2BuyYt0VQQYlOe2nl3kz1aPcGdufz0A/3Zy2Q2eR+cL\nn0rTMuhEjtfzQjfUJdlEowsfTiPrgoagltUUcGA4OmJDMGoDhCQxBSFczVEQybRKCALqpeb8RcXQ\nhJ6NffVAUWWhlSPiev1IelHLgm+8dmVrY6nP+Yz3ZsZZH1byDwx+23Gf/zo09OBX4LXSmT4NPm6g\n1xay7CKgF1aovYeNvBqljQmQnalg+w+nfcM6duyN/5oQ/zubf6dUee1KpiS1g2IC2LCQ5Ezu5hvf\nw4eSltpq9sUVWfVCeUAGgrBA0jiOWhIa0qss4Mg0q4akepMEiMP5is20goeKCgLyEM6ruM3X6Iw3\nLwP7vgbo6N0KcMOc+/Cz3xegS2s3H3gpsu+KM7kf4MkhglkRqxlvgN25Ans8u4IeWuMWij6bAwZK\n27yLIgDHYMnVURdHYV1FEYgjAXjsFWTRk2jL1j710peGg6VZWegaPj/mv5cCCARGqOtbV1ZC3xsr\nG5nsF1vTb2y9P0ENP/wo/uPsOMzHO3KjiWD5zCqsr1oGdnmSa/kp647pMLoRbOm70Ts3BS8/IvR6\nEuJ9o3s7Jm1Abj16+lbyPFDpFulpMnjEgxVG7h7zeWCFbpA4gWaXANDlhogZcpXBVNw1oS4PAgos\ncDIGTUvAl5EdloedTNPmBWMMQyaVFVFXLK+lVcHor7xPGJcjSeqmXiGWP3hMltUntmycFHnugSCY\nmq8vBEdof4ku2zOEgYWe9pdX2xajlclsBXhIl3ZQw6Ft24Us3lFt2wQOKemm7AcXh9+9Tdu5vin+\nS/ojaSJX3S8XfnzDNTd84CXmCHDadrz7OuB/vQl31a7XF5pj7712u0+QnTzrS8PtL7NdxakVbNee\npoqz19cHt+OJlVqsKMyU8uDl1Wh6w9uzN5keYrOzx8nef+3wxw8VP5z/2hPqPYBXHKCmwb6Do4E7\nPvHBgEEBPSdAMkbwAR9AEUozvTAmu5yDOpSO+Wy/YKOkn0bqoLyx1dCxdaKd3chjAZZRY4jfz1O4\nGoj4wOII1CzQ/9Xm3DGqZToP7uc2bbshBgLcptjJOmjPdfssPbfsVOwY14x38bjG7Ubj3N4Iswia\nukvqLdcwTcmAIBtxAOoFrhPCUFAC9/Zsn7/+m/KtXLdnYWjz5F92wHft+NCbL7176Y7X/gEQ4vBT\nX5Fp/v7w49rOfYub2564/B/zu3Z004eVGNzai8yk9h3KymvBUSwwnBi4R59chqb42ZHSAthzUI81\n9nO7zuXCG9mlGv1XPby8QvwBDbzgvQK8HU6Ya0NAaMPGLcMw1DJyQNAlM+BxlCwAsJIUnKAXxUow\n5lBQXaJJA8cNwxYQwBc6POGMt2Kli0QdEl1LY6ssCISgOqQCXyNb2fiL22tJPhSwjxQzewab/X/0\njd0698usCib66kFmHcfFajVNqjVVgJZrC+mWMIlL3YABZkuhFK9CI5ZicTqDwY5taC3OsS1wmZGD\n9yFDoNHHupdf6L70XEC8cXLbnvM7xuMfBcK78D+73523nyHWD6Q/eWca/7vKhvtuPdu49bIxD1ep\nd3s3s6texCHq/Tv2d0NvX5tHzPe799cObgbZqa2lE9fYw80l9NlI3/763Vt8fom8NPObu7wegF6N\nzbDVdJV1IEZurpkTWg22Gbqao6kEcH0w0qKqTZzmfLKvItOAsEjdS+TtlBcAukVWfJqOBddx1rBo\nFzcGLVDLaBVPL2g5bQPXRrGS3GhU0Qad2tm78q89aOfd7X/b0Q7EbAafGGLHiK7dLCU5VNi3u8c/\nINCJNqYJLAvYHpNweAO4rGY5imk2XFogdBSjAVEqlbXRdcAWnkyWO+7aMHCR3gdRQ8ZiX+LXoOH9\nVP7yi7z7l39MLTqHQfA/Q3v3vyxt5N63M3MvbKW7wjflFlJrZ1Mj5xFFUTq6BVleBeLMGsC3lbmP\nHkECodDJ9dPNtY4dyPGrSPML8r1tNRjkBgtMVc8v5PgukM7gJYoYdktFXKZhXxrAlmjrNouCFlmm\ndRIRY3XYcFt4QoUBMIpqiuDpGFSD8oBqKcONoKZgqsoy5SxYz4fHPf5L7lndSF86e0P+30uX55rE\n19aKv9VmwDYzCbrr6jXyeCVNuI7D69NVf7EG0atSHBCQg0HA0lowysguYhMOztmIphNNDIBdszun\nC2caGKkdopu5lWtC/LjDV0qeteD6HSDvPgfaN9Q6yO095/6YnMgInaPdty/3dP1w3+kBmI1PMFHC\n19lmrKZ/dvZqJhoyt2hcqroUR3rAnw1LvpaLzgVaw8JjO7j8a78Yaq2Lz95HnXvZC/hmb0wCNh0y\nq02jNIUPeio1Z2DVD5QZHuCNAExZDtLUbcHL1yBf022omm66XooGLiqYkCTbmseDumyByvRTzYjj\nofOmRgNl00xhYsjiqBat7+rcNBautX9hBv1COuqJ0mAkUsvjjLmhvb9AL3BZy3PvCDvPRapStFcB\nKrBM1YPrMOKoPlxGMWBKsGRBFoKhID84cwDfccEZFf/FQOBrjeqZAAULGiMTHVfAF/ueMe+9GZ9Y\nwfwP+a+I3p7+xspFz1uvPhDs2Qvb2obpCRm+hUfJsaHtvkDtrD+rN1TBmpiFwYM/8D662ihjOban\nb28BvbY+hnV8ivByTOFnr4MKupAS+BZ+VeUQm+v0VHSKYteHJoOSRwA509F1rEYTJmjpuleXXQ/O\nwiZZ1tUmgClvoCkETMeAKAjfWGtPsVgqTyAhrx+A4Mwu5fFs7JYxMtfP7GF2l/QN8kDxD6i+//VB\ncBm3Q6YJ2jP9hwLg3CpRUCUGRVpwdk1aAJQLo6hicy6C8DJMy4AkKYGAIRnVRXBNPKj1Y8P1Weg1\nKaDmyJMfkug123/Ni3lY8Nup69lvS7Y62fPDFPLjMf5RnJ5bDCeghfPch9GOc6yzcXaLXG6xgWag\nVRcepOC7M1l7jGm/CZqHyj8bTmXi9dJo5qlQwnv7eulGWdG911v/9MmnQlKHPkf1N/dkCATC6LwL\niflApzg0HYQaICSGZKsE6zjkaLZBQZxmMg5lEG1uAQFowQcFdLwWaLVNMRptaeEWX3W6lSpnaABB\n3+SlyHAtnU+xt7DJO7ew1yn5YeGGrm1sAioAzLUR+71ec73jVljMrA/Y5e4CHACyHwEm5sisq9us\nRMqCrpOqReEybKGIxWEgGhD/60lHWH0p6Qvp0asGdIrvGItfMNe96yzY0q+pjQPi8UdLj//P5rTf\nfpuIhLEycwDCb8vB1WCvycTJ2Rx5Y2VuZ31N1Rnxq3VKuZycAOCmTey46JabaLxWenRu5n6vN7JX\nyg73OMTgcwDiwHp3AGMXrHKJNRy201cfCqRU7xBh0QDCVQfyQ7auo16fQyEuhRg85mtlyh4ZOB0y\nZNslRbbWMUJOe2tSAw5411Qf5JNA81jQYudX/BvKLLmjvm++erW2cdeVHaGjXm0NeKtyLVAMb6zj\nvaEDg/6hTRgKo1sdzsFrYaBpsCBDDiPjqFtHTBSmEJVGIDJquhA49dt/PuTOTbXf9gFNrE566A+m\nyVfpcXkr0+yUgIy/0c7/zZWfGjrxQ/+x4fn0B4fISOXsn1Z++t0FmJfWuu30zR0bwiPc5nesHZhz\ntfJ/LbiLOVV1we3+jlL7wVTsbHrotZ903zz1Dj/cFD6PvO6HWRRA5oqDy1WZVdLYhNzKXm20Kb0j\ndp7XMAAcL2s28ZCjo0gl12XyDYAIsuQYFCtRwLV0RMR0hG1CPAx4X4KQdBE16LmGFQKxr63cZmQ2\nzBTAkeAFkliCI5vn87duwaJ6GAXrhpoXJflmKO27WVnrlOAqqNpp1mTNgAooFFdJmzIo2DQdxCFN\nWHcUm2kZKGWBE++7I7s0uO/s39XmRGr/5v4/70+L7/VsHN2ZSD0ETit/8RJx9+Nv91SUn29+Iftk\nz8J5+7mp18bAXemO/9dv4P/7oA/15hDXApRb7YXXeb1BBlOJarRuJVdD9LcfiK6Sqq3yiTQecgrY\nbI/sjVZN0YqVYq2XzwszhiDiLX4qiqnxFW8M49O0LWtw1W989MMuzBO2DBu8iHbMddlpGnc0riaE\nSmrwn55n65TkYi3CXyRJ3LAtGjTCYjCDxerIQyMfLXsv8IeulwbCqasd995Y2JU7f/jVQMATvesr\n53bu0kZ3ntNmTt4ZGKRnp6V9kxvtnV+3D1048zHiqb/OoBqFu66IobhKQRaDpbkmXvc1rT7ll6eA\nR5a9VRgJlZBIEWHllhLRZCFUlTzC0B8wdrFM914bkyY2TTvtfCHz8acjYTGyqtEFmK42MQFr0g2v\nWfXYTFhvBnBPiWV1wloH+bIlC6YfTMigkabNISpk3FIkmcI0ogJulvrgUjAGI4FI1JBiuPvW9SV1\nXGwzFVsFnX1oq1UXyLDE9a05czIRwKJ0EFkyMWMBtMq2RqmuFwM+BVSLHg9XjznIGk+kFQ3sXeDW\nouErjbHV5x85mJh6pzvf39/6SMcnQ+M/McG/Xtr9m5O/Oev9VOKNykNNiboRyt0a3xFaA0c/tALS\nboHuLDso1c8YXJnqbMmGMSglBuOEKwAAOZjbYmAXMED0oLrj2VB3PULKy2hl8OZ1nUj+/5q1Dwa5\nzsJQ2O/pdWbO9L6zvWtX0qr3YjXbchHYhgvGENvUUEISLuQmJCE3AUMSuMClGRsXjIVxk23JltW7\ntCqr1fY6W2an1zOn1/szvu/5HY//4SfKpVj1gVU9ufrhKWbfMhhD/FILTKKRSqrKgIBNwbpdM2mX\nDeUDeL1uaCxoN1hogoaYHts2PajPEPJQA+H04BKzTABh6Ww4eMqIV1xj1Vik7qJ3KUJ1R9ashhIY\nGM+QPj6SV8X80rLuDeAgmbtHLzNdtQoXAAIKk5mY7iBlGK8RIZQ1QF71NFhuwZbBRDip76CCnkXf\nti+0n7mE5lzXPvHL0sJfXC3+x8Evwo5/vtLUkO95s//m36nPBHvT2dDE7NqWpu+lGwCJxnLnuRUc\nv5ikIFYazeci2FJEmxS8qXFAQyrpRVGZETOWs0xrhDrJSpboS6ooC7QtNp2MnHk7G1QGTi6kdUey\no/h8oRvl+JY5GMlUA7APLwN3la/RlmmVDZItz6O4gpcB7Mu72ul8ToxHWVQ3PZ0dppuoiYwFAwfY\n7ghXig3le5pw5M69e/qp8azSmBlyzgIvgIGCVjREJXi9JVrKoCZMxGzYNIpSxiEBEHaRvBcIK2JB\nsbV4NCobzSGqnq7Pe3AK7FB40mNMVZZdsy0wnvhS7tTpY/0L0YXhzp5bYNx99H0q6zy/5kfjZu3x\nlUZqh7mHlOGrlTdNGBS9EIiowTqymsVJBWdcgQKkq7iO63YMKFQhy6NEUAFExAyWUdEKKGYA5eNN\npg3un/JeU2+f87ZWqVfbInzgfvd7Jh8rtOWvnSrAOQCXYUhC9SLpCooIrxSlmtvXJuRJNASKSz5T\nlR0esDRNTyo8f/kuk6aanBhPBytgOU3FuBBC0qEbYc3Mojrc5nQ3B9uJqbsK6PY5Kp287ZJKnmBC\nEeoEE49CsGl5clAVYCiAqYrPlCnZCObyJQeB6EYGUci6Cwcc7wOH3rYNz4f4gxe+47n4QP/24S3E\n1nWJ989R4H9nPz3XPmD/7YNX17/XNx6ZQ27nf5177A3tUWXpBsBXVBSqVKmSDLJZLiPj+VyZYBVL\nsJoFgOUZgiDLHI14bMyiZBmV3G5Lw+ySAQHIPO6uTunEcif1ZCfVTOkrta71Xumq/cBaFxxm4kGE\ncxoEamusBxbymQnDHYETca5WAJKpyQJKwLinedE1nJ2twgwkyfV5OCvHAM1IF9EcsMkF1rWp2hsB\nGt6IYGsKCR/rBdUksEd1wcRaDJxXmWpqQiqYFl5EWNwPDIMxlzhbMSXMlHGCMeLC4rRkQmxrTQel\nzR+Bv/k5/vQ3n6zjiYWz7YWmj/61yyXLvVRDBJT3/oIb93iHP+/d/cOrdNZMrn0We6A80P7PKfEA\nyHJ6u+GnccMIah4f6250JQgcdjImM9QEpE4vY+kET9okKbK2pYsYhvMu00zTObCwyDUG1U0QcaWg\n34Wme2cW7B4xlGbXLK3cByMODQCVCVe0ssMicQ0nUcLHAaRct2mAO2jZxytQScwYFZeBtAUVh96w\njYYxdAakVxFOyVu4s+yykDFvNS77fXJH9VzcLizGgUnZHr83iKuGUAqjWJMjFMDkgIurNuM20LJV\nn3NFpxKUhVfUUJ0TcndGpDwQUrAfXD77JKplF+/+cqXfxsM/ij8g/cqMCnbjNCYOgqP8Z0pBYcWc\n/WbriW9eHEqsZRbWDE33I9sfPl4AVJHWQpSVbVDkCM/bynAeNfmgD/PlokuAKeZM2wxqkqQX+UrN\nw9VrZRmIsuJWDTDtnLoN7qsHlYfJLJt7IDfONlaH4MedE2kyBxdrZcVtQ5rmcvIqAreYozTbKtVI\noBScAJvXuWqPO0/bUs/BbXSLigaq/qwSlEJjraBpxYyvkNbjnJi6zrZUSz1lZa07ipTuuEMMcAW8\nGCeQCstXiaVF4IAMM4Cgbss9Y5DA145XPfGwM+2oZKvlORsI924kCEJHbD0Fgk/OmbHkfppqnGrh\n7km3/u0iZr3Z+d9kaAfcD7TYWPcCLB/Z9tepz70VT0z2zM7HxtVbPHXun8NgIw3ps8UGX12AKJyM\nIryPDcFLVSfbaGugjlAAd4skhTOZUpXDLYrBCVmhBQRvAs/OE3hr6VRq68zFD+bf+ewDP5RfXwwM\n/txTZegwGipxdVu2sC6bHomRsclMVU0ER5EC5IB04ApBaq2y5OE7hVoJDeeEpXycl02jeY5FgC0R\ntx3aCjlLKt2uWkBIs767S+5hLQapSwDNw/QikUnkFA0K9GYm8ZCsVDXbXYnPdAKlbOhISXcQC0bJ\nFjEi3vsLe9W4ilm0WwTnKU/7yKF3n6kRX/r9bs01MDB5LLFxcMel2mOlIvjeT371TJzicpm2E8yu\n8Ct/87OBTfgPJ+xFLeS4DGZZpeYmlorNOGlUtc5CuObF3GY6JPj1BeApIQShOg2yS7eEBLMMyV6S\nIKajVa4kgUuNh+4Oy9vif2mf86QSwKn84T+vtJWyYSPRysNVrGzbRFQWKqMkH3WTDpzeOzxs+QlA\n1wBPssWgEsHhGlZYmZpXeX18WkYwGfEDBLCwsLPTTMy02ev7wv0LBY1K2fGzrpjOFVyA13FVxVvr\naBXB0NsZpDpjWRjuUNkUWgJ1i4cytiauSDXKxwhoy9g5f9RjU5RLtMGBf7xyc/LLbddti++hOs0R\nSWQ018CeBFV0d4IfNXzriLibZ7FQk//oG9/+9baD4tsvjQd6Tgwu9AOaQjsIBgoHXaXlQCjvSmtJ\nJkgQvL+iBgFgyTJraICqTSZxAtZEV5ava+GCzRMycLW/vLIbLk0zF+7/5NPPQHu/9NnM15n2DUhj\n9vgCbNsVnXAuwQqtCplKrSa4O0IjLiyjYmUGOOp5/2xSdnjU6eSMpmkazpUX+Vw2rXVPgYk0PFOM\n0N2qK+HSz0WKtO42L/GtI3j9kRnAkabgtefQquUm6nmjoJkl1UNU6m4WsgEJRTHOYsV4PBpHHVFt\noVJ9wDCdlqUpCtj+DZ7b/E0LudFxPXzdP5t4OhI4rM+cXNetPlQHnxk9MNl/ghg6PLdmW3f76w+V\n/7TU6FNff+3rz1w5CSQYZIWsIOsoGiiIml2vgZK2oldn7RkV1BQ8NidW1dxbFwbTuFFUUw6bUwFu\nMOUQgKcebx+EPdQ95vDBtR/dt3E6FXeNiq4++dAhG3ZJQc7I0KzDQJBqqVS/s2HTxYVFS2UVrwzq\ncFBlGpQcqPJYU3tP147NLeZUVexwOeEIaO6gsfV1lYDAdAF3zxndcVf6vDbehHNTVTAtORmCs8hw\nUF4sdXT19kI21VC13ElAQ4CPSy4Y8LAU6/T1Mkuu0kzMXAGw6Ak5/OAj/YfeLDjaueqiQ+/+0K6k\nP4OcsfsSL6VnXp0Ehb7B58fDG7j/RD46X7/aZbq3zPfd4puhubOtD4I+XrdcWEDJlAg4HG0nvUq2\nnsKWJVwLA4CSQGPwUPrc6z97b3Q0Y+F+AQZGhoWKvjSwA8d8C1SewZ1a8ujUSjrZkTiOax9d2DCf\nVFEnpOaimuhns4wGZHi5u3nrn2ZWm7BFSyioMXU9YAR1bhFmOvoDmOm+kKoY5oo/r/DgYpO26Taw\n8nA5NuIu+3ub6uLdU383UA4NcQ3AQ0C2roQNifEsbwgFG4Yhr1HW3VVWMQOAmgzXFCDTlNMI3BaY\nknMx/FE0mIhrKS8JHMD1nsO3+/rdTT22/HBy4CeOKuWsOVGJwFovLVZjPxmsLlW3J9eO78tufJ4Z\nLJ898lai6cmThg9M4FBVhWMNpJUiE/HVH/hTNS9T9bHpAKcCgMtqTz2vLBqN5rVEoCHjcgglgFY0\nt+kH8Py339oKXXD2qyejol81mts9I1RxI1yBPBI8MocwVY0KwWXIw/mLpdBqVzVhYAgQRB2YzghN\ns/XaAhOIr1lDDVv39VBWnTdtCrFAJG7OeHHZy8LCUg7qXVUZ/PMPN4pkrdDvbQYINWMt66IFYdzT\nz3zhwRjTbLmpVZhQR1srwPYsUhSJ0JKu5G0FipuxHUxNEJ0sKRaBL3hp2xfDAAZ9VWB+59CCvn7V\ngjrf1dRzXb4L2vu7R75069ydyQcvNvNzv4V6443jv9+VjSUzv6sACPfTEZCHzDLqXfOUQ1qBRFhy\nQ2ZIqavAqddiiwhdRzfsa731xxrKoGVZLqKOWN05CdS255sXXR2AplWyzTx7lA1/iG+X2jcVu6NN\ncIwqix7LrVc1o4dYmC3wsMvf5pYpnQqYAE6doQhg4SlXaPXA/LtibjRNiKoK2QzvBLjVjhqi93xh\nvVxe0RRxtDbT9MRAZZyZjVWAW/Lo7aJkoo6GbPL9//ivWyW/UUhnA05/KQlYXskWEFnWKlRtpcRg\nrg33HVjbTC2n0DoFpreNQCe2bWsK/srb/sfn3sk05jJmA//r9kulAgdmw4OGvCvUV/jjpyvZz9Le\ni373nu+N+k/erRAeUCIWMKPBQygRnU8kj9+QV8oZzIgCxC9oIOfipKghE8rGLncRDjv8oLq0RKgC\nb5RCQA7Ers5Qk1bmYFg7Orrnu9M/Ke5xxy6/5XxtwUBLLG6lQxxrsu1JoTxTo8dP4w4LhMolFACn\nuG9covE6QTZEl1NaaXNxNtfOWQZ1y6kDl3FvIGMH1w7Kyvbq/lXj/HztiKtF7nB68hkw79e8usuE\nQvX6/DvJodbdqFWm7YJGaDYHqphL8optBWdRT4Ooq+Yww0RWwVREJHUgfTyiO8ez95Ttr2/Y8tH2\ne59960DTr7I/+uOnvv7KCIimbxj3ntX14FpZaXhlq0PRq8mNfBo71bTbDRrqLgyvNy2pzgA4y4+c\n5ZpYVrflGlGImMCnaEAWZRWqVvL6gM2Uq6LlXQq4BVbhwIaXGvrjdx75becPNlRDu8+I7s3yEkJ5\nGl86UNiMhmXeVlGVakchd7rS7q1DV2HRZBeBq04C07jXuWBWYJmPRLLkHopK3iEoxNLQFkQCWQqZ\nCIDMTL8RzPVRFd9FBTyZXfUncGNVygYtNiEgORdklCaWlvKR2FykyKBLsClJZABwdqhoctOMTtWo\nICxIy4qZUBGYAAJVAOGbu9HJG4bbmY3kBuzuxu/801zuy49NTH3QibWMsm1MkW/uDf0ix3iLh95c\ne2Z1+9aXH7zj7rvvWytgskMjyWpeMssUxM8XAy63L2JRiuLPGhioazSmBpfzpY8rtb/bwcpqVSDg\nuIOUeQ8C7j74Lr8FWanWDo+g//jRg2OTuv7Vny98F1/HmbdQC0PqZq5JWYwqJT2u0HpuDaimzHxU\nIWDgrLQv2whdDTBFyZewzZPDNQ9GBZYmwssdoCvbVemfWuiiF9ph3FZnAh9KvvIxKO+hsAhIEQw6\n32L5i2ULSuwydVp0ZBpRzIuN+UwgKxNxQTJ0WhC8jJqxqrI3Q0BVyqCUOIghm6XLyidece668mO/\ndhb0Fa5trO1/7Yn8MbUdyD89MHb4ypH5y4GLDmxq7UOj2/7cIr/Tf3zfyQACurJRqUxhEpzjIjOF\nrLPBq1vGisZonEACzkRzVo1rBtnIgN0rzZdpU9R0AnaaDhLgr//jiyvuyl6+vj9+1Hy96RPZDWfk\nJ07v6x/2BeCCzuBcCMtAev2ehXBO1AWrSiPfg+IVCIAwK5Iw4tDzizfevTp4Lms7g1CkFhKx1jIo\nFdGBrBCwGOh8xphio9KNpclRyNzfrzUyAItrdTfBWHV6wtEJiyFCS2tZPyYv9VYEIFFRi4MxtwFz\nYb+KAq8nbhM1n0nYqAx2uc/d3t9K/rCx+ek7r9RwSDE6PiyTtz4mDzzdABz0qZ5k8mX0fzIXtjdd\nGviP5ndKH0cT0e+OTDbbQPDOowThcPmQ2nU5FVzbR3QLJc5dtpCaBlISZImuJl//U5/cGarWQZS1\nKYpERJmvFYBPwkO+yqV6jGtb2dH/pMPXfdbXynV/h4ykMrC7rDBAVaJSjY81oGyFSOsCqJPzgm3m\ngCQle+2MxkQJ3GAXc7UCPADFeCXXPIOUQKlPqUwH4ULuxmV+LEM7+3KBpVyGWa5XoCRAUGsGz9Gm\nw1obwjxMRcYUXAOYFMpHLcDW4iYm0YOFMcSq0TCLtUb9ZBOcr1q8AN7wFjftghwvdswvzbdfZtYi\n637YeXL1F9bq2buTAJoPRA13/XYHeXjM+VDqMLVn++fx0eCf+57uGgCYRhosjERDEcpX4GIsE1yE\nG9wSWfc6WOBFyl5arhBxFLlQzORpkyYJGiMkqB2SwZmnLqBvCEF1V6fy2m9emP4a8m5xxFnM7nxt\n3zYaFljYiYsWHLIyK7pR8i0p0xnFUN1YkfACmsdmUK1cEr0mh+LlGhXl4fIipasdoAZ6RqHF9ryD\neC9bnsCR01atvVjv6xc1temsC2AInSA5rZnz0hbEc6alqooPVOqYNIMBO5AzZESyCrrGBRHNE+jB\nBTtVa8bwIAHco/t/P0zNPTXfu9rnwB78qK/wpZGH6vSv+xsdd0Hw0w853hrsWPogRTl+Nn/h61l+\n38lF4t6h8X/7QAOw7QJD1ZKFahAq9gQpXqXY8m0n6Ux5LYAriCnYGu1kxHqu5tPbHPEI5suxUL4M\nAzRWVh5Ce72XL7nwQT999/jO5uX89OMbvv5iRkeRrFel5DwECbRT92gGhwDJRPCCxZgpsNRYgUWG\n1x1lHZY5yE8s+yEXmcWXIC0xidUCtSrlABuvIJvgqi9b34XMYNk102WqXwH0UMKbctujeMWPaqSC\nQ0JvPeWSW9LdlSywCy5di4pdRU3M22bcR4qqT/EKyywG0gB3HNt4BV7zPJ37xQs390+59F9G9yYX\nzjzxfyPJePrDmY8f+fKH1ObykV/O7k1MP+n6mcUuPHZ6ubcZKMDEcwyuhCTZoa201CCvwRuoEcp6\nV0jcArIH5xHeafIwzsg0iBiiJYEsqupQXANNpz75smEgvYrrvc2oz5kYGgo86L33n8hrfe4cWo4U\naZmx84U4QlZLeZsQMhSgy0jEsEngznI4imRAklPEWtWASLxe9VUaTJcOFHCaTq+epcc3XLwv1eO7\n6RanOIIOlMeobrlYA4i3osTgYsIILNumphmxWNV0BlXLU132AZ6uulXNJ3rHCmUX0rAM09loxaQa\n6jraCByFT9qQ4Rzweb98CuOf23k/8Ly4xXP/e09GH/gDuP1I80+6E/fSG4dXLSaGjty7+9R54Ble\n+v5b1hwF6lyixuFZq+oPc3ldgTiqoNmaD24Q1SqAoFrdDPAWwwleNQrERd6CJY+ioVbNBmz41LLL\ndVvtnd4Qd9OnZ7uo+09X2hf7fbSgwz6rgSQoVYeXJCNgmjjtT4SxGstWQMEGdszOjS01kTEMKSgE\noRjSApsN1OqpAFsBEcRTizOrF1xN6/niLhe64QjTjNcdrFt1BkAp5Xfrrlrd0ExjuURwGMXgkTIh\nWlKzDuQyDMFcNUZt7oVgw65bVtwgaAh2wEAB75RuSEul36RL8O+nzMy/cy9NlCjDQXy1/IcyCipr\n3t1xOLKrc3R5V+dY7XW+PHlIJBb+5r2Vt6m3Ab0iWBSqkVFN9ni9XJ0vcToM1ZMVSvYBc1nTIEKH\npAIGR6VSVsEg0iubwKmiDpAwPOvvNDf3d2BDVJVaD8yxYQSeaFdujgYlmIZrkrwCbJjljQrnAFK5\nrCicaZgK4wGIZEQbKAXyaDa5seaAZGytM8xQelutEgSGTVYuXVlegSA/snLvumROmDTf4rEkxk8A\nf8ginOWO/KRGwb1ro3Fr1tKnaRctF1IOEAjDVbIa46GS1WwnVFomeAitUQCwPhOs2/XQXOsjvKde\nju/cdGPw9ys7364UC/rxcHsdAf/6kQZNWKVob+W/y5c67l/aQQ4cSbR40xt/2vgNgIZqhijgumna\ntTos1Q0MdtqIW6MzOAZsGo76SaespTOCajgAixk+xOF3CGpFBq/mlozOoah+yeO9MDU1q6C973/o\nk6MdXy2dsOCFRrTowh2RqTojICmL5pyYqdQ01Fk164CtWGnLXDBzrKGMUgICc2pqZcigDc1wg15f\nEtveoft47bwsmRZFrW+FUhHRWuK9TlCFtVxdWAk7HM1tfTZe8WwgomFJMVeqORJYhkEjCiSaJl4i\nK4bm0rHFMqvUaWGZAMPwjFyc8wmhSd/df1WfXV14s/0Lpy9lOku33j4EblQlb8NycNnZZOR3NrKr\nj6tfG0JKv93k/NpZHqgAh7AErBGQPIyrhBdB2Ypli90ZPAYB2KFkSjM6pTlpwcAsokqG86Rdz2t4\nNAgOYLCgtRTlBl/JqRxtNGQmuxt3FH+32OSvw1QNoldknG9VSvmcoXMmzUGuPKfCJAIBCIZCuOmy\nTAjF2QCk5UqzbBCUTajoyIAJhMud29NAtE34IAcT1bLXUQ/ISkx3cawF2JDNFFSgQSBZSzpxvYTg\ndRSTzN5QIgnssocmQSns4lgf5aU92jIbpxCLSPGQCu5jjk9ZmAtGWgq/cC39bVIauHA6dJ8Qb/qM\nfgKg7Yj56uDM9bnKrRh19Ecf7ch861i2ddfwmU9+OQ5gzlk1l02uJHh64xCOiMQ1ooyKd1lqwQBo\nxUmTBI5htaIVVUQHh+OcqMANpLySA+XB9XWbyX8K80SFBktAq5WNHTfLNqrvjURgmmexRkzMsipp\npGPxtCkqwGoQvZUQJYKCKOp1VhB1XqJNSRE9wGPrDhzUKBcLsMWaPzo5xkBbmBJBh2enxqolVQow\nyZbgJIhLusFyHggXRBLPZqjYIokKeV0uYzoJZEjgqxJe1wwHHShYaqFpWcVYk6I9ARSMvvqNXTcC\n09m5Xu4rgWXqe8Ff7+14NuXI/PH5T3mADt8n7OmqEsT1R25f692z+3ri5XjM6nJ8+vjUUeCbgVmU\nVspkqJatBIwKttRhJyhXr9MiSFCLUUDHDB6mbW8KgtU8UmB4w6cbDTAF/EfS4db57nf4+VLO3Ocp\nb7xTeGWzPt2Xmlie+v/6Dfz/H/TwpnRhc3J8B5hONzdWQ23pEWLu/mOdp790F2N+/N0HTvukgdGu\nn20KlXp/1/nwNMn7kkLkHqdunPjng2T1udEPm/nsgHfJcS/XQBXoLXMs95+eI42XX/j7xGsbhN5y\nab3/3X7t2KOLkSGjsHvHS113d734l8TutLvK0iZc14Ka4FLoEhtY4bRMIO80/ad+eaJ/vtbOKtx8\n41zfzcb5RqPjZHbVB08Vpcb5oy+0XNg+W/RZ/ae233Gcf1jLEbnMjpzbc4OVMi8vr+C2ylooKSKa\nz8TmEK/G8gRXxExXcsc/hBS8QLoVoOC6FVcLnJ0nOTaPpCLO+t9+FVoDNLQaeutbNxF4tOd620tP\nJ+V1VKrv1PSj8OaPdkxk1O1FwbmlzRTvHFfiFfnSxvJf/UvVQEFiOAaU42TyaapuJL+w4TiKillH\n3Lcx0GOnwKHqw78Jr7cGPdNvjVcbmzuW16Q/9Jb/9P2+/FUBfAx9A2p31kvQu9U6W/vhxnMbE6vB\n8MFzXEX7b5BFCBZUYBgTLYTOkiVQmxRyUsCGdTYLzsmsSYNKUUvQk2Wve0+s7Tz9GdeBk1l73AbV\nNzrPOcDLUGn9Zfpex9xwjWyML0YkDsdauwDPyJTDgpCqS3bnc2YcL0MIMFE/gvM0IAzb7jLKjGVr\nBl5mQ9miAxgqR2tGBgGtxr2JhC977Mgkp8JbP/Qf3zaZXv0m272yY08G3vMY+IZA1Lhs6eJ7XXt9\n0Zmr0QThMn7/VX8vBUwJPm99wlGb6ZY1NOtoKfF4yu6oqe3nIRyEnhp3vTfo3Z+710pjlEmGgsyj\nv+p2v9u1sDUHmr/7knVzvsZdIR5RAu2fH2bfnjnRWnA1j53/0mbgBGWTxixRacZKEqHbiAeBDUWU\nY7iIA5loYwN2N/PCxVe6B2PzNyu3u8x7SFufGna4gbpvtAX2frM+nYyttH2K2uLTb8SD4u6bCfmN\nWwCyW0iaJnFUc1h0ICuyrFm21OIUYrNhEDQg28Q4uxl3qBgyVI45y5YlimZcdNTAx77VAomH9mPn\n2/8JWezY1jSvHCb/6UyJlfw1+FRy2o6HBfwwpDQuLV4RwWNZDX8vsrqE633g7rinUfizjCtz7U9+\n3EbCjNr+Cc8b17ZiT7KbwRvHV/L07tpkoLdCZD/uez/3/P0Xvl0lDy1xtV6w7Yexva2La1rn958Z\nmkL7cmbHBesi+taaaH/1KqgGNHUJFmCXKFgUy8Mg50QaIy4PzLAI+B/f/tPIrdzdVKDRlzlEV0Yq\ncNIslXqxgqr2gPF3hHFhstezJ8i2Q0dte+7cYXTf6ImYEX3SDxQlQ7MQkGJYsSprQBZdxTqBF52m\nVrMAhmPlet2W00TVpWoJvoxzWL3qqLkZNQLkT6ngaNm4kb7v/J+GV4h3Gg81yTWzo1ycPeuGL6Zn\nR3zKkZu19Zv+eov6GVt/F1dKzUiXdTL7Logeueup+cwE7K3c2rLobtPDrvrc47suJnND3eCAT/IF\n81uI7rkGrH8v+MdVm+6tO65rKw9sQxRwo+y+I0hA+AAAGIhJREFUS+XuZL0Xyn2dGxbH7J2f3JTG\n/a/1fKSPAFJASNxVZxST8tVLhAGQup7TM6WUgthg8q2WraWGDHjI5Wk+E+Se1NC/jvZDI5Gu/fn/\nBX5woBLkd34w/rvqY2znpvhw8/8azb6ycq1YNdAi8BuBxaqq+pfqYRJOo7RXpXyAaPLBMmaDKgNR\nujeLlW1WcNKoD8EAhlk1LU9VRDDw3PADvmNomzR8S3rwbkvr1ze3zPbf8WKHV7sA3HXPbCXe+SXy\nCy//1sdv92Of2f5AODFvL3KfABHQOWhjn3MkT0yh7lup6JfntiFTp4VXSNCYMl4C1py3kSClxDyU\ncSW1pmVG6r+46b21wk/fHN8JNlzrwNMb14mfnttU//coeehTbz/7je6W6zb1nNYDXICybSEKG14Z\nczC2rqpQUOZdwA1UBqSrwLGlf50hcXuu+mZYK7Duj6QExthwevt3wEthB5n3jWzF4n990Sfd21gU\nVg88+NCThDBR2w6Wmopk0HYVcaeke2OEA2NJMixmVzINLASsokWpSEB0axRXXi6jHOIMORxMK0pE\ni+DqlqbJm3srzVV2r6j+fFyZmLEPiCMQNnF+kxd2dyETi7s2tkSGH+rkf2Rs/zl2Se/4lLDWMztm\ngHS0Mb8yHif3E3eJysXwG+dXjpGB5kmH0Ne9CYx1PnsWn1I+Cva07WWxQPbyVyvt/gdd3Be/sPkm\nOEVmfjawPGzegLHkww5P4Hbn4NFqlvsX8EK9B4h6mfLacoav8XVM1RDLJVkBtO5aNi0eZFcvv9B3\nN7ndHyx9BY0EHMHsdhFGHZfNyUAD4JbVBHXuC+/gqU2x6aBshAZHj98S54pYmQkDchmQEiinYdly\nYxiLOmQWzjopgxLKKWB6tbqTEEkXDhOCVyuXFAJ2c1aennVg4NFzzsEDo4mkv2OYb36/YSU+sOtc\n1Bb4hcZsFSZ3rq76sm7SaY6PUKacicRzP7xHKKXzW/pJADec33tp69Xuu9cj9U94+8P1FBuq8V2S\nNDPPAHTpffJdeGr16P8IvvpIR7bt8XdqO6MRTXnx/R8fBvu3bXzqm1zZz+2s72/8nSvMNV1lMnda\nv/+B21kHBk8jkATDhEohRZSGyKqDgMxAgZM0GTwyJv6AOd9hDEdPx7ZEmcY26RydwHKqWdOfA1sz\n21zB4Oy3m4LdUn/LE3V9w0Nhp/e+lYb7F18CNKYgGqEGcFHCTLGuLfr4Okz6o2HM4QARC4l4WAZU\nrUzVh0fdVF0uKLosEVRBAcOPDm8YfWDJwQpN4YnP64+OzWc2F/ZoR6wqOw6b5Zo82Ez5PxHXu8JG\ndXDP+eav+i+1nHGeHfGDNcWHLq89sT29v+tOt+Zh2gj+gAfFzk+fnkGzoHXbmqe2pyrN5kvVjc+X\nsshCYs+sIPSuwuEDR0FQeFM4/0SClGJrV3J9F24LC0/QW0jvwaalSgE4W0NFyiFBDG5IpEp6MUuY\noyMio3phBry8eZtrwp3UP/nKrgXv0me2H4dbvMfG6PtKDacpcJyYejv+h+JZIXI0fuwPGDY5cb3e\nAF3Vps+WnwZ6gLRlhGbkgFPBDKum5wOoqUpZWVNtkMsD7U5ZsykFl1DRwItoqirVIbPsIepg1bDX\nSbyzPIuYbVL1krN9+i3b9l8KD8Kc0AIvobXIONctjmoYSbVtDR/+Su+66848TSBeGIxYFWbsO6mN\noxWngjuYtcuPBvBMtaVrSyCZBAuxG+eFZ+qtTV/DsUdpf7Zj8d6N59rIwpcNxyFgiiPK6dEb091v\npFro76XQDbvF9uAWL8pYO6ZAWoT8wiKHU6RiQiRJ8AXcJ1FM3WvYJbAN+vGy/6BXWm5drMPgwje3\ntl/r2rRu/+D2P594FOw1o9H0Qx353R4V2bDjrPWgUHEhV1cfaeouTwOH4EYEAAyXRKpiQJKiAoAq\nYhFOZ5wmEMkqtAqr8SWhomiaVLUrwGOymSxXw7uBOsHhgYN4t+0bnUnsMs8o6z+WX962TqYrPgkV\n+epWctQQuKVdR7/x7jJ25QPvetLzfjtwuJ4HcZBranz16Rt9r6UpK1CcpFMU73dEgqepvSq4yKoq\n3rD6jlXf9OGM59ljUtfx2FfuXN969dP/VQFvzH6NXBa83vdDW67ewjf5kGD+fPPxhq++7TzVCGhV\ntTwcXoJhq+Kq6HVXJMdoFThRIFgmOXf3q7+LdGzLnGr9/vOV+TWrFkAeMmeaIsVV3fdASjr38FDH\nQdcdvlXRStt40MvwxP1X4rI/kQelsFhqzAiYhsJQHgnnS5xTCfikcsjI+ABGiVWp4LNKQVGVGLTe\nwhs2VXd6eToXBD4FkoXTn3lV6954U5ro5Q+2/eCLkdEPNc+qshd+vL+YVztcLeuRys5jkmOdGHAt\nzDhWKvvG9kDg1aVkDXRd5G/W2p7yXj3RclCfH+6KVkstvQsUcMcIL7jFWNiZE88eeOAtZBXRtHcV\nFcYr1W4YKOtOKcp8+OkB35sOrJVuhk2xMXCuWl2OxSYAgTmIil1gQN6CUnRfB2OxZbVCGUZdkMDs\nGvSLtgOM+8WXPzWG30xA5Pm/2BgNw41bDoPFK4edvlU/M6X7EyBXD6fgy4UrvxM+sdyjxiLANGCf\nGWv0UYK9wvmsIE2u6Ei1hIkorQCnymmkg7RYHlbCikXUFUsWCMvMQjgBbgTAeMaQ7muk347dS1ca\nVwYfej7mktdPLIseeOWmdLTFurDwvsdzqlZbPQv35n/d+OuscXLg4vfAl+R1ZHzcIfn1PsTAE8VA\n8n2vUfcHqbqrE3yytkurrV/Y+m5r7jcfOMor2RsmcqoBnSDf/2wSHNw5veXGKuZCfnm1L5F7S7k6\nMnKc/6H0it2jRQGm10pOCgQtFmrb/IXWBz6HFynRtupaTGBB5+jI8X8YuLopLK+6vHpTZT6KfG2g\nZ8fpSZnBPwDcTq/Qn01e3Dyhb+eupaf8m7SGvzp9DDquqBTwK4ZWShsG7sRgQ7FkXUZN2aAhmDQx\nIFtVdwnVKBuU6ws+2lYcLsxFmkYcdRfAwJ6J/q7QrZkDex9elUAbOZq/V72c3bWSKIivw+elXU9C\nS4G2htzKkUD/Rw//VOL3vT+wzWPdG/kIlEnTuuZIBkaRw6o/vIu7dLY/YCJ3BnXFdRKUF/5CsR86\nPM+Odu/dc6eP+vBgt7CEL7VSnac3Anwi/lJvbC/o8pUWi8wbquja/LltykNLod9tIAFFABRxahKE\ne2KHfOrBbhedlk3LJgoYDOp4+53yneLtWFd1fHr+E/Nk99reE1oHfb10swzy2qjnjZk+94zgUirr\n8/9+aSE7+0of9/e9wWQWVHXBy7lgU0MyhEQAE5FQhFQUxS5TBrBBlPcKVlaNUu2UsYBKjJjSs0hQ\ntQsVoEwbrmant6WUnsocmOJOvFYgmq51fljgC5OfhEMZajZH/3Xq5v5aR1Zbf7L59kPl9YGervyu\ndgeY18/vbJDy6NTmyp0Vl94nb8TbBRlrTrkXpkEKjUJtSsEuN00Pf7wGdW1Nn/U8vnyIzqStNeDP\n9NqaOiFP44+Mb/SOzmW6XfLvh0LqU8uPXP0W0A2kJJhcRi/RLvKy7b1WM1hM5CGDC6XBWuO5/R/N\nOTcP5kejzpG3H7l53ncUy5a9/q36TnCwUT4f7u1bE1q6KLRdc06uObRO35eoviDc2+sEFmYXKbrk\nJmyfz69pep4QYMaJOjRDsoHgKQQqvOhqILEabtE2ybPRFYdr3sw1h0DPlw/S/PW5Y01kE3roiQWj\n1Ra7uha/8Dm4LCnwyLMq3Xbgsnjg6sCQh12mdwy8df/WseIMxSaCAA+0ng9Y+6t7O24HhHc+zji2\nPLqM5m4Vu+r018HFVWu5pL/r+f6pJnbNH64qM6edrxbwXkGn1l0HrSxz0VMZC5/8+VMAuJWWiavd\n/zToq50zvRefAJIq9blgBKEDuCP03Z/cGDSMiMJCAjRleMC7rf+xpuUgi3aMAKzY6Zvk6JurHLdz\nfujilgLQXH07nrnzb/IvSKuivEjlQ1PpSDLnW7WuNZkErFSnzCJGFQjTzesWASoetKagCg3DCkBL\nTJEOaC5gUk4NpnRs2aqzVmmVyBVk8Oob1I9aY/a3Rye6yecWKyHCBH9cm/rpb8PBr6ThlsFh8eVp\nQ3+lI0c80rh6E547XL/w6Ra0/DX7PRAVYh031s54OFpZSIcbjLsrQeTExJYsx83fAHsvX04sNlQP\nEDJtTx4E4p6N3jIdmb0dbTo3DQbwt3s7t/P1wIH3LvYPlbp3zp+9C00NpXYSX3wMIFigqNWqqK1m\nLvzhjef+/ZbUlo1giKkmCjXQnQsHx36cGVUP93I1vGeV+fpKeOsGMMnNniyDD5L+pRn2bwrPrN5m\noc81U2fle3uGeOsoqHf3AIj08BXNBYfSRNoQJJmkNYdkapiGYShwQRZrQWipqCMahsEym9AMw99Z\n8zsQGhxcuu5/ReyTa+XxU4Pr9Vo1UHl0bBd69DdsVYZby8TC6rvB+DdHGufkN+nVgplh6bvD6xw/\nkJtBITKm91jOCdxslMTp+6r2YMDTtIHWP1qCl0HZN3R53ykq9+6OtSufUSg0xS201N+seigY6wHp\nfOfu4g+Wxhukneq9+86f5R+qdm4afeyhk/fOYUBXVaKCKCyoGRMfvvriXIp1c4KR9zMVpw6S+fcT\nvQ+3MPxcwQelkZ98eH+PmpltVNu6yBKILS2sm7NPKHWslDm1I3OyYYLIP2a+bnzkn58HQhl2wB47\nKSFIA04xKONXiriHtix9wQAaxgFdsdxsQV1YWpL8tMnrgfq0CousAf7cyPRWHyzN0uy5zsqyGzjQ\ntStTNwa+uuHJiVXwuBra2yrNDpbdyTVHP3H7pTQ5c7y5n6jPF275gcVG3uDpKgYn+WtE7zn0kR2/\nf2kJ7TuSaNjEgEHja8lKh8nu13/20MoJt/PSmcamqwnbkxzyVMHHTmUjHWt+wHRCsWOTurB04yv/\nnp/yv4l5tp0FBoLoVaSJKDtRySFKRCNUcwf8pO4mIQI09kvvdWfwlU66PZd75na8bZUP/5hr3lO0\n3A6QHBg6j8pwMkm5nrkfE73aTv4WOd0ao6alIHBTDgTgCAGctMwgTpJPkQ5WUjBYTQjANlIVN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      "text/plain": [
       "<PIL.Image.Image image mode=L size=289x289 at 0x7FAABFC2AA50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize Filter \n",
    "from PIL import Image\n",
    "\n",
    "def scale_to_unit_interval(ndar, eps=1e-8):\n",
    "    \"\"\" Scales all values in the ndarray ndar to be between 0 and 1 \"\"\"\n",
    "    ndar = ndar.copy()\n",
    "    ndar -= ndar.min()\n",
    "    ndar *= 1.0 / (ndar.max() + eps)\n",
    "    return ndar\n",
    "\n",
    "def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),\n",
    "                       scale_rows_to_unit_interval=True,\n",
    "                       output_pixel_vals=True):\n",
    "    assert len(img_shape) == 2\n",
    "    assert len(tile_shape) == 2\n",
    "    assert len(tile_spacing) == 2\n",
    "    out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp\n",
    "                      in zip(img_shape, tile_shape, tile_spacing)]\n",
    "\n",
    "    if isinstance(X, tuple):\n",
    "        assert len(X) == 4\n",
    "        # Create an output numpy ndarray to store the image\n",
    "        if output_pixel_vals:\n",
    "            out_array = np.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')\n",
    "        else:\n",
    "            out_array = np.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)\n",
    "\n",
    "        #colors default to 0, alpha defaults to 1 (opaque)\n",
    "        if output_pixel_vals:\n",
    "            channel_defaults = [0, 0, 0, 255]\n",
    "        else:\n",
    "            channel_defaults = [0., 0., 0., 1.]\n",
    "\n",
    "        for i in range(4):\n",
    "            if X[i] is None:\n",
    "                # if channel is None, fill it with zeros of the correct\n",
    "                # dtype\n",
    "                out_array[:, :, i] = np.zeros(out_shape,\n",
    "                      dtype='uint8' if output_pixel_vals else out_array.dtype\n",
    "                      ) + channel_defaults[i]\n",
    "            else:\n",
    "                # use a recurrent call to compute the channel and store it\n",
    "                # in the output\n",
    "                out_array[:, :, i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)\n",
    "        return out_array\n",
    "\n",
    "    else:\n",
    "        # if we are dealing with only one channel\n",
    "        H, W = img_shape\n",
    "        Hs, Ws = tile_spacing\n",
    "\n",
    "        # generate a matrix to store the output\n",
    "        out_array = np.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)\n",
    "\n",
    "\n",
    "        for tile_row in range(tile_shape[0]):\n",
    "            for tile_col in range(tile_shape[1]):\n",
    "                if tile_row * tile_shape[1] + tile_col < X.shape[0]:\n",
    "                    if scale_rows_to_unit_interval:\n",
    "                        # if we should scale values to be between 0 and 1\n",
    "                        # do this by calling the `scale_to_unit_interval`\n",
    "                        # function\n",
    "                        this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape))\n",
    "                    else:\n",
    "                        this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)\n",
    "                    # add the slice to the corresponding position in the\n",
    "                    # output array\n",
    "                    out_array[\n",
    "                        tile_row * (H+Hs): tile_row * (H + Hs) + H,\n",
    "                        tile_col * (W+Ws): tile_col * (W + Ws) + W\n",
    "                        ] \\\n",
    "                        = this_img * (255 if output_pixel_vals else 1)\n",
    "        return out_array\n",
    "\n",
    "# Visualize filter\n",
    "w1 = sess.run(weights[\"h1\"])\n",
    "image = Image.fromarray(tile_raster_images(\n",
    "        X = w1.T,\n",
    "        img_shape=(28, 28), tile_shape=(10, 10),\n",
    "        tile_spacing=(1, 1)))\n",
    "image"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
